diff --git a/2-Regression/1-Tools/daily-bike-share.csv b/2-Regression/1-Tools/daily-bike-share.csv
new file mode 100644
index 000000000..c41c9f4dd
--- /dev/null
+++ b/2-Regression/1-Tools/daily-bike-share.csv
@@ -0,0 +1,732 @@
+instant,dteday,season,yr,mnth,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,rentals
+1,1/1/2011,1,0,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446,331
+2,1/2/2011,1,0,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539,131
+3,1/3/2011,1,0,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120
+4,1/4/2011,1,0,1,0,2,1,1,0.2,0.212122,0.590435,0.160296,108
+5,1/5/2011,1,0,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869,82
+6,1/6/2011,1,0,1,0,4,1,1,0.204348,0.233209,0.518261,0.0895652,88
+7,1/7/2011,1,0,1,0,5,1,2,0.196522,0.208839,0.498696,0.168726,148
+8,1/8/2011,1,0,1,0,6,0,2,0.165,0.162254,0.535833,0.266804,68
+9,1/9/2011,1,0,1,0,0,0,1,0.138333,0.116175,0.434167,0.36195,54
+10,1/10/2011,1,0,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41
+11,1/11/2011,1,0,1,0,2,1,2,0.169091,0.191464,0.686364,0.122132,43
+12,1/12/2011,1,0,1,0,3,1,1,0.172727,0.160473,0.599545,0.304627,25
+13,1/13/2011,1,0,1,0,4,1,1,0.165,0.150883,0.470417,0.301,38
+14,1/14/2011,1,0,1,0,5,1,1,0.16087,0.188413,0.537826,0.126548,54
+15,1/15/2011,1,0,1,0,6,0,2,0.233333,0.248112,0.49875,0.157963,222
+16,1/16/2011,1,0,1,0,0,0,1,0.231667,0.234217,0.48375,0.188433,251
+17,1/17/2011,1,0,1,1,1,0,2,0.175833,0.176771,0.5375,0.194017,117
+18,1/18/2011,1,0,1,0,2,1,2,0.216667,0.232333,0.861667,0.146775,9
+19,1/19/2011,1,0,1,0,3,1,2,0.292174,0.298422,0.741739,0.208317,78
+20,1/20/2011,1,0,1,0,4,1,2,0.261667,0.25505,0.538333,0.195904,83
+21,1/21/2011,1,0,1,0,5,1,1,0.1775,0.157833,0.457083,0.353242,75
+22,1/22/2011,1,0,1,0,6,0,1,0.0591304,0.0790696,0.4,0.17197,93
+23,1/23/2011,1,0,1,0,0,0,1,0.0965217,0.0988391,0.436522,0.2466,150
+24,1/24/2011,1,0,1,0,1,1,1,0.0973913,0.11793,0.491739,0.15833,86
+25,1/25/2011,1,0,1,0,2,1,2,0.223478,0.234526,0.616957,0.129796,186
+26,1/26/2011,1,0,1,0,3,1,3,0.2175,0.2036,0.8625,0.29385,34
+27,1/27/2011,1,0,1,0,4,1,1,0.195,0.2197,0.6875,0.113837,15
+28,1/28/2011,1,0,1,0,5,1,2,0.203478,0.223317,0.793043,0.1233,38
+29,1/29/2011,1,0,1,0,6,0,1,0.196522,0.212126,0.651739,0.145365,123
+30,1/30/2011,1,0,1,0,0,0,1,0.216522,0.250322,0.722174,0.0739826,140
+31,1/31/2011,1,0,1,0,1,1,2,0.180833,0.18625,0.60375,0.187192,42
+32,2/1/2011,1,0,2,0,2,1,2,0.192174,0.23453,0.829565,0.053213,47
+33,2/2/2011,1,0,2,0,3,1,2,0.26,0.254417,0.775417,0.264308,72
+34,2/3/2011,1,0,2,0,4,1,1,0.186957,0.177878,0.437826,0.277752,61
+35,2/4/2011,1,0,2,0,5,1,2,0.211304,0.228587,0.585217,0.127839,88
+36,2/5/2011,1,0,2,0,6,0,2,0.233333,0.243058,0.929167,0.161079,100
+37,2/6/2011,1,0,2,0,0,0,1,0.285833,0.291671,0.568333,0.1418,354
+38,2/7/2011,1,0,2,0,1,1,1,0.271667,0.303658,0.738333,0.0454083,120
+39,2/8/2011,1,0,2,0,2,1,1,0.220833,0.198246,0.537917,0.36195,64
+40,2/9/2011,1,0,2,0,3,1,2,0.134783,0.144283,0.494783,0.188839,53
+41,2/10/2011,1,0,2,0,4,1,1,0.144348,0.149548,0.437391,0.221935,47
+42,2/11/2011,1,0,2,0,5,1,1,0.189091,0.213509,0.506364,0.10855,149
+43,2/12/2011,1,0,2,0,6,0,1,0.2225,0.232954,0.544167,0.203367,288
+44,2/13/2011,1,0,2,0,0,0,1,0.316522,0.324113,0.457391,0.260883,397
+45,2/14/2011,1,0,2,0,1,1,1,0.415,0.39835,0.375833,0.417908,208
+46,2/15/2011,1,0,2,0,2,1,1,0.266087,0.254274,0.314348,0.291374,140
+47,2/16/2011,1,0,2,0,3,1,1,0.318261,0.3162,0.423478,0.251791,218
+48,2/17/2011,1,0,2,0,4,1,1,0.435833,0.428658,0.505,0.230104,259
+49,2/18/2011,1,0,2,0,5,1,1,0.521667,0.511983,0.516667,0.264925,579
+50,2/19/2011,1,0,2,0,6,0,1,0.399167,0.391404,0.187917,0.507463,532
+51,2/20/2011,1,0,2,0,0,0,1,0.285217,0.27733,0.407826,0.223235,639
+52,2/21/2011,1,0,2,1,1,0,2,0.303333,0.284075,0.605,0.307846,195
+53,2/22/2011,1,0,2,0,2,1,1,0.182222,0.186033,0.577778,0.195683,74
+54,2/23/2011,1,0,2,0,3,1,1,0.221739,0.245717,0.423043,0.094113,139
+55,2/24/2011,1,0,2,0,4,1,2,0.295652,0.289191,0.697391,0.250496,100
+56,2/25/2011,1,0,2,0,5,1,2,0.364348,0.350461,0.712174,0.346539,120
+57,2/26/2011,1,0,2,0,6,0,1,0.2825,0.282192,0.537917,0.186571,424
+58,2/27/2011,1,0,2,0,0,0,1,0.343478,0.351109,0.68,0.125248,694
+59,2/28/2011,1,0,2,0,1,1,2,0.407273,0.400118,0.876364,0.289686,81
+60,3/1/2011,1,0,3,0,2,1,1,0.266667,0.263879,0.535,0.216425,137
+61,3/2/2011,1,0,3,0,3,1,1,0.335,0.320071,0.449583,0.307833,231
+62,3/3/2011,1,0,3,0,4,1,1,0.198333,0.200133,0.318333,0.225754,123
+63,3/4/2011,1,0,3,0,5,1,2,0.261667,0.255679,0.610417,0.203346,214
+64,3/5/2011,1,0,3,0,6,0,2,0.384167,0.378779,0.789167,0.251871,640
+65,3/6/2011,1,0,3,0,0,0,2,0.376522,0.366252,0.948261,0.343287,114
+66,3/7/2011,1,0,3,0,1,1,1,0.261739,0.238461,0.551304,0.341352,244
+67,3/8/2011,1,0,3,0,2,1,1,0.2925,0.3024,0.420833,0.12065,316
+68,3/9/2011,1,0,3,0,3,1,2,0.295833,0.286608,0.775417,0.22015,191
+69,3/10/2011,1,0,3,0,4,1,3,0.389091,0.385668,0,0.261877,46
+70,3/11/2011,1,0,3,0,5,1,2,0.316522,0.305,0.649565,0.23297,247
+71,3/12/2011,1,0,3,0,6,0,1,0.329167,0.32575,0.594583,0.220775,724
+72,3/13/2011,1,0,3,0,0,0,1,0.384348,0.380091,0.527391,0.270604,982
+73,3/14/2011,1,0,3,0,1,1,1,0.325217,0.332,0.496957,0.136926,359
+74,3/15/2011,1,0,3,0,2,1,2,0.317391,0.318178,0.655652,0.184309,289
+75,3/16/2011,1,0,3,0,3,1,2,0.365217,0.36693,0.776522,0.203117,321
+76,3/17/2011,1,0,3,0,4,1,1,0.415,0.410333,0.602917,0.209579,424
+77,3/18/2011,1,0,3,0,5,1,1,0.54,0.527009,0.525217,0.231017,884
+78,3/19/2011,1,0,3,0,6,0,1,0.4725,0.466525,0.379167,0.368167,1424
+79,3/20/2011,1,0,3,0,0,0,1,0.3325,0.32575,0.47375,0.207721,1047
+80,3/21/2011,2,0,3,0,1,1,2,0.430435,0.409735,0.737391,0.288783,401
+81,3/22/2011,2,0,3,0,2,1,1,0.441667,0.440642,0.624583,0.22575,460
+82,3/23/2011,2,0,3,0,3,1,2,0.346957,0.337939,0.839565,0.234261,203
+83,3/24/2011,2,0,3,0,4,1,2,0.285,0.270833,0.805833,0.243787,166
+84,3/25/2011,2,0,3,0,5,1,1,0.264167,0.256312,0.495,0.230725,300
+85,3/26/2011,2,0,3,0,6,0,1,0.265833,0.257571,0.394167,0.209571,981
+86,3/27/2011,2,0,3,0,0,0,2,0.253043,0.250339,0.493913,0.1843,472
+87,3/28/2011,2,0,3,0,1,1,1,0.264348,0.257574,0.302174,0.212204,222
+88,3/29/2011,2,0,3,0,2,1,1,0.3025,0.292908,0.314167,0.226996,317
+89,3/30/2011,2,0,3,0,3,1,2,0.3,0.29735,0.646667,0.172888,168
+90,3/31/2011,2,0,3,0,4,1,3,0.268333,0.257575,0.918333,0.217646,179
+91,4/1/2011,2,0,4,0,5,1,2,0.3,0.283454,0.68625,0.258708,307
+92,4/2/2011,2,0,4,0,6,0,2,0.315,0.315637,0.65375,0.197146,898
+93,4/3/2011,2,0,4,0,0,0,1,0.378333,0.378767,0.48,0.182213,1651
+94,4/4/2011,2,0,4,0,1,1,1,0.573333,0.542929,0.42625,0.385571,734
+95,4/5/2011,2,0,4,0,2,1,2,0.414167,0.39835,0.642083,0.388067,167
+96,4/6/2011,2,0,4,0,3,1,1,0.390833,0.387608,0.470833,0.263063,413
+97,4/7/2011,2,0,4,0,4,1,1,0.4375,0.433696,0.602917,0.162312,571
+98,4/8/2011,2,0,4,0,5,1,2,0.335833,0.324479,0.83625,0.226992,172
+99,4/9/2011,2,0,4,0,6,0,2,0.3425,0.341529,0.8775,0.133083,879
+100,4/10/2011,2,0,4,0,0,0,2,0.426667,0.426737,0.8575,0.146767,1188
+101,4/11/2011,2,0,4,0,1,1,2,0.595652,0.565217,0.716956,0.324474,855
+102,4/12/2011,2,0,4,0,2,1,2,0.5025,0.493054,0.739167,0.274879,257
+103,4/13/2011,2,0,4,0,3,1,2,0.4125,0.417283,0.819167,0.250617,209
+104,4/14/2011,2,0,4,0,4,1,1,0.4675,0.462742,0.540417,0.1107,529
+105,4/15/2011,2,0,4,1,5,0,1,0.446667,0.441913,0.67125,0.226375,642
+106,4/16/2011,2,0,4,0,6,0,3,0.430833,0.425492,0.888333,0.340808,121
+107,4/17/2011,2,0,4,0,0,0,1,0.456667,0.445696,0.479583,0.303496,1558
+108,4/18/2011,2,0,4,0,1,1,1,0.5125,0.503146,0.5425,0.163567,669
+109,4/19/2011,2,0,4,0,2,1,2,0.505833,0.489258,0.665833,0.157971,409
+110,4/20/2011,2,0,4,0,3,1,1,0.595,0.564392,0.614167,0.241925,613
+111,4/21/2011,2,0,4,0,4,1,1,0.459167,0.453892,0.407083,0.325258,745
+112,4/22/2011,2,0,4,0,5,1,2,0.336667,0.321954,0.729583,0.219521,177
+113,4/23/2011,2,0,4,0,6,0,2,0.46,0.450121,0.887917,0.230725,1462
+114,4/24/2011,2,0,4,0,0,0,2,0.581667,0.551763,0.810833,0.192175,1710
+115,4/25/2011,2,0,4,0,1,1,1,0.606667,0.5745,0.776667,0.185333,773
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+120,4/30/2011,2,0,4,0,6,0,1,0.4725,0.464021,0.503333,0.235075,1965
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+125,5/5/2011,2,0,5,0,4,1,1,0.459167,0.441917,0.444167,0.295392,614
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+127,5/7/2011,2,0,5,0,6,0,1,0.52,0.512621,0.54125,0.16045,1612
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+157,6/6/2011,2,0,6,0,1,1,1,0.678333,0.621858,0.6,0.121896,673
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+159,6/8/2011,2,0,6,0,3,1,1,0.775833,0.727279,0.622083,0.136817,676
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+163,6/12/2011,2,0,6,0,0,0,1,0.6925,0.643325,0.747917,0.163567,1467
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+671,11/1/2012,4,1,11,0,4,1,2,0.365833,0.369942,0.581667,0.157346,466
+672,11/2/2012,4,1,11,0,5,1,1,0.355,0.356042,0.522083,0.266175,618
+673,11/3/2012,4,1,11,0,6,0,2,0.343333,0.323846,0.49125,0.270529,1029
+674,11/4/2012,4,1,11,0,0,0,1,0.325833,0.329538,0.532917,0.179108,1201
+675,11/5/2012,4,1,11,0,1,1,1,0.319167,0.308075,0.494167,0.236325,378
+676,11/6/2012,4,1,11,0,2,1,1,0.280833,0.281567,0.567083,0.173513,466
+677,11/7/2012,4,1,11,0,3,1,2,0.295833,0.274621,0.5475,0.304108,326
+678,11/8/2012,4,1,11,0,4,1,1,0.352174,0.341891,0.333478,0.347835,340
+679,11/9/2012,4,1,11,0,5,1,1,0.361667,0.355413,0.540833,0.214558,709
+680,11/10/2012,4,1,11,0,6,0,1,0.389167,0.393937,0.645417,0.0578458,2090
+681,11/11/2012,4,1,11,0,0,0,1,0.420833,0.421713,0.659167,0.1275,2290
+682,11/12/2012,4,1,11,1,1,0,1,0.485,0.475383,0.741667,0.173517,1097
+683,11/13/2012,4,1,11,0,2,1,2,0.343333,0.323225,0.662917,0.342046,327
+684,11/14/2012,4,1,11,0,3,1,1,0.289167,0.281563,0.552083,0.199625,373
+685,11/15/2012,4,1,11,0,4,1,2,0.321667,0.324492,0.620417,0.152987,320
+686,11/16/2012,4,1,11,0,5,1,1,0.345,0.347204,0.524583,0.171025,484
+687,11/17/2012,4,1,11,0,6,0,1,0.325,0.326383,0.545417,0.179729,1313
+688,11/18/2012,4,1,11,0,0,0,1,0.3425,0.337746,0.692917,0.227612,922
+689,11/19/2012,4,1,11,0,1,1,2,0.380833,0.375621,0.623333,0.235067,449
+690,11/20/2012,4,1,11,0,2,1,2,0.374167,0.380667,0.685,0.082725,534
+691,11/21/2012,4,1,11,0,3,1,1,0.353333,0.364892,0.61375,0.103246,615
+692,11/22/2012,4,1,11,1,4,0,1,0.34,0.350371,0.580417,0.0528708,955
+693,11/23/2012,4,1,11,0,5,1,1,0.368333,0.378779,0.56875,0.148021,1603
+694,11/24/2012,4,1,11,0,6,0,1,0.278333,0.248742,0.404583,0.376871,532
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+696,11/26/2012,4,1,11,0,1,1,1,0.313333,0.339004,0.535417,0.04665,337
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+700,11/30/2012,4,1,11,0,5,1,1,0.298333,0.323867,0.649583,0.0584708,362
+701,12/1/2012,4,1,12,0,6,0,2,0.298333,0.316904,0.806667,0.0597042,951
+702,12/2/2012,4,1,12,0,0,0,2,0.3475,0.359208,0.823333,0.124379,892
+703,12/3/2012,4,1,12,0,1,1,1,0.4525,0.455796,0.7675,0.0827208,555
+704,12/4/2012,4,1,12,0,2,1,1,0.475833,0.469054,0.73375,0.174129,551
+705,12/5/2012,4,1,12,0,3,1,1,0.438333,0.428012,0.485,0.324021,331
+706,12/6/2012,4,1,12,0,4,1,1,0.255833,0.258204,0.50875,0.174754,340
+707,12/7/2012,4,1,12,0,5,1,2,0.320833,0.321958,0.764167,0.1306,349
+708,12/8/2012,4,1,12,0,6,0,2,0.381667,0.389508,0.91125,0.101379,1153
+709,12/9/2012,4,1,12,0,0,0,2,0.384167,0.390146,0.905417,0.157975,441
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+711,12/11/2012,4,1,12,0,2,1,2,0.353333,0.338363,0.596667,0.296037,282
+712,12/12/2012,4,1,12,0,3,1,2,0.2975,0.297338,0.538333,0.162937,310
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+718,12/18/2012,4,1,12,0,2,1,1,0.410833,0.409708,0.66625,0.221404,433
+719,12/19/2012,4,1,12,0,3,1,1,0.3325,0.342162,0.625417,0.184092,333
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+724,12/24/2012,1,1,12,0,1,1,2,0.231304,0.2589,0.791304,0.0772304,174
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+727,12/27/2012,1,1,12,0,4,1,2,0.254167,0.226642,0.652917,0.350133,247
+728,12/28/2012,1,1,12,0,5,1,2,0.253333,0.255046,0.59,0.155471,644
+729,12/29/2012,1,1,12,0,6,0,2,0.253333,0.2424,0.752917,0.124383,159
+730,12/30/2012,1,1,12,0,0,0,1,0.255833,0.2317,0.483333,0.350754,364
+731,12/31/2012,1,1,12,0,1,1,2,0.215833,0.223487,0.5775,0.154846,439
diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index e69de29bb..8a71517b3 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Welcome to the world of Machine learning\n",
+    "###### create the .venv environment form https://scikit-learn.org/stable/install.html\n",
+    "###### use the .venv environment for the execution of the ipykernel\n",
+    "###### first install scikit-learn, matplotlib, numpy, pandas, ipykernel in virtual environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (2, 3, 4)\n",
+      "3D Array: \n",
+      " [[[ 1  2  3  4]\n",
+      "  [ 5  6  7  8]\n",
+      "  [ 9 10 11 12]]\n",
+      "\n",
+      " [[13 14 15 16]\n",
+      "  [17 18 19 20]\n",
+      "  [21 22 23 24]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = np.array([\n",
+    "    [\n",
+    "        [1, 2, 3, 4],\n",
+    "        [5, 6, 7, 8],\n",
+    "        [9, 10, 11, 12]\n",
+    "    ],\n",
+    "    [\n",
+    "        [13, 14, 15, 16],\n",
+    "        [17, 18, 19, 20],\n",
+    "        [21, 22, 23, 24]\n",
+    "    ]   \n",
+    "])\n",
+    "print(\"Shape:\", a.shape)\n",
+    "print(\"3D Array: \\n\", a)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2-Regression/2-Data/notebook.ipynb b/2-Regression/2-Data/notebook.ipynb
index c9b9925ba..ca45c5b13 100644
--- a/2-Regression/2-Data/notebook.ipynb
+++ b/2-Regression/2-Data/notebook.ipynb
@@ -1,5 +1,353 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build a regression model using Scikit-learn: prepare and visualize data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name             0\n",
+       "Type               1712\n",
+       "Package               0\n",
+       "Variety               5\n",
+       "Sub Variety        1461\n",
+       "Grade              1757\n",
+       "Date                  0\n",
+       "Low Price             0\n",
+       "High Price            0\n",
+       "Mostly Low          103\n",
+       "Mostly High         103\n",
+       "Origin                3\n",
+       "Origin District    1626\n",
+       "Item Size           279\n",
+       "Color               616\n",
+       "Environment        1757\n",
+       "Unit of Sale       1595\n",
+       "Quality            1757\n",
+       "Condition          1757\n",
+       "Appearance         1757\n",
+       "Storage            1757\n",
+       "Crop               1757\n",
+       "Repack                0\n",
+       "Trans Mode         1757\n",
+       "Unnamed: 24        1757\n",
+       "Unnamed: 25        1654\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "new_pumkinns = pd.DataFrame({\n",
+    "    'Month': month, \n",
+    "    'Package': pumpkins['Package'], \n",
+    "    'Low Price': pumpkins['Low Price'],\n",
+    "    'High Price': pumpkins['High Price'],\n",
+    "    'Price': price\n",
+    "    })\n",
+    "new_pumkinns.loc[new_pumkinns['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "new_pumkinns.loc[new_pumkinns['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "price = new_pumkinns.Price\n",
+    "month = new_pumkinns.Month\n",
+    "plt.scatter (month, price, alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumkinns.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel('Pumpkin Price')"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,24 +358,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.13.3"
   },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
-  }
+  "orig_nbformat": 2
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}
diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb
index 2902cce87..e3cd14ce6 100644
--- a/2-Regression/3-Linear/notebook.ipynb
+++ b/2-Regression/3-Linear/notebook.ipynb
@@ -16,9 +16,209 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -32,9 +232,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_11800\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
     "\n",
@@ -71,9 +391,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x227dc6b56a0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "plt.scatter('Month','Price',data=new_pumpkins)"
@@ -81,18 +422,643 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x227de8d4190>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
     "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_11800\\3995499251.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n"
+     ]
+    }
+   ],
+   "source": [
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "colors = ['red', 'blue', 'green', 'yellow']\n",
+    "for i, var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    df = new_pumpkins[new_pumpkins['Variety'] == var]\n",
+    "    ax = df.plot.scatter('DayOfYear', 'Price', ax=ax, c=colors[i], label=var)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n",
+    "pie_pumpkins.plot.scatter('DayOfYear', 'Price', c='red', label='PIE TYPE')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between Day of Year and Price for PIE TYPE: -0.2669192282197318\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating the Correlation\n",
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n",
+    "correlation = pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price'])\n",
+    "print('Correlation between Day of Year and Price for PIE TYPE:', correlation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 144 entries, 70 to 1630\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column      Non-Null Count  Dtype  \n",
+      "---  ------      --------------  -----  \n",
+      " 0   Month       144 non-null    int32  \n",
+      " 1   DayOfYear   144 non-null    int64  \n",
+      " 2   Variety     144 non-null    object \n",
+      " 3   City        144 non-null    object \n",
+      " 4   Package     144 non-null    object \n",
+      " 5   Low Price   144 non-null    float64\n",
+      " 6   High Price  144 non-null    float64\n",
+      " 7   Price       144 non-null    float64\n",
+      "dtypes: float64(3), int32(1), int64(1), object(3)\n",
+      "memory usage: 9.6+ KB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_11800\\3144308612.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  pie_pumpkins.dropna(inplace=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pie_pumpkins.dropna(inplace=True)\n",
+    "pie_pumpkins.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients: [-0.01751876]\n",
+      "Intercept: 21.133734359909326\n",
+      "Score on test set: 0.10220539781539772\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1, 1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train, y_train)\n",
+    "print('Coefficients:', lin_reg.coef_)\n",
+    "print('Intercept:', lin_reg.intercept_)\n",
+    "print(\"Score on test set:\", lin_reg.score(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model determination:  0.04460606335028361\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = lin_reg.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x227dfdc7b10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test, color='blue', label='Actual Prices')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x227dfe95bd0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, pred, color='red', label='Predicted Prices')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Polynomial Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score on test set: 0.12719946902474621\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "print(\"Score on test set:\", pipeline.score(X_test, y_test))\n",
+    "pred = pipeline.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70        False      False                     False      True\n",
+       "71        False      False                     False      True\n",
+       "72        False      False                     False      True\n",
+       "73        False      False                     False      True\n",
+       "74        False      False                     False      True\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738      False       True                     False     False\n",
+       "1739      False       True                     False     False\n",
+       "1740      False       True                     False     False\n",
+       "1741      False       True                     False     False\n",
+       "1742      False       True                     False     False\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.28%)\n",
+      "Model determination:  0.9653029999448537\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies    (new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "pipeline.fit(X_train, y_train)\n",
+    "pred = pipeline.predict(X_test)\n",
+    "mse = np.sqrt(mean_squared_error(y_test, pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "score = pipeline.score(X_train, y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE  Month  \\\n",
+      "70        False      False                     False      True      9   \n",
+      "71        False      False                     False      True      9   \n",
+      "72        False      False                     False      True     10   \n",
+      "73        False      False                     False      True     10   \n",
+      "74        False      False                     False      True     10   \n",
+      "...         ...        ...                       ...       ...    ...   \n",
+      "1738      False       True                     False     False      9   \n",
+      "1739      False       True                     False     False      9   \n",
+      "1740      False       True                     False     False      9   \n",
+      "1741      False       True                     False     False      9   \n",
+      "1742      False       True                     False     False      9   \n",
+      "\n",
+      "      ATLANTA  BALTIMORE  BOSTON  CHICAGO  COLUMBIA  DETROIT  NEW YORK  \\\n",
+      "70      False       True   False    False     False    False     False   \n",
+      "71      False       True   False    False     False    False     False   \n",
+      "72      False       True   False    False     False    False     False   \n",
+      "73      False       True   False    False     False    False     False   \n",
+      "74      False       True   False    False     False    False     False   \n",
+      "...       ...        ...     ...      ...       ...      ...       ...   \n",
+      "1738    False      False   False    False     False    False     False   \n",
+      "1739    False      False   False    False     False    False     False   \n",
+      "1740    False      False   False    False     False    False     False   \n",
+      "1741    False      False   False    False     False    False     False   \n",
+      "1742    False      False   False    False     False    False     False   \n",
+      "\n",
+      "      PHILADELPHIA  SAN FRANCISCO  ST. LOUIS  1 1/9 bushel cartons  \\\n",
+      "70           False          False      False                  True   \n",
+      "71           False          False      False                  True   \n",
+      "72           False          False      False                  True   \n",
+      "73           False          False      False                  True   \n",
+      "74           False          False      False                  True   \n",
+      "...            ...            ...        ...                   ...   \n",
+      "1738         False          False       True                 False   \n",
+      "1739         False          False       True                 False   \n",
+      "1740         False          False       True                 False   \n",
+      "1741         False          False       True                 False   \n",
+      "1742         False          False       True                 False   \n",
+      "\n",
+      "      1 1/9 bushel crates  1/2 bushel cartons  bushel baskets  bushel cartons  \n",
+      "70                  False               False           False           False  \n",
+      "71                  False               False           False           False  \n",
+      "72                  False               False           False           False  \n",
+      "73                  False               False           False           False  \n",
+      "74                  False               False           False           False  \n",
+      "...                   ...                 ...             ...             ...  \n",
+      "1738                False                True           False           False  \n",
+      "1739                False                True           False           False  \n",
+      "1740                False                True           False           False  \n",
+      "1741                False                True           False           False  \n",
+      "1742                False                True           False           False  \n",
+      "\n",
+      "[415 rows x 20 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -106,7 +1072,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.13.3"
   },
   "orig_nbformat": 2
  },
diff --git a/2-Regression/4-Logistic/notebook.ipynb b/2-Regression/4-Logistic/notebook.ipynb
index 7c2127636..923c74b53 100644
--- a/2-Regression/4-Logistic/notebook.ipynb
+++ b/2-Regression/4-Logistic/notebook.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -211,7 +211,7 @@
        "[5 rows x 26 columns]"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -222,13 +222,430 @@
     "\n",
     "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
     "\n",
-    "full_pumpkins.head()\n"
+    "full_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 991 entries, 2 to 1698\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column     Non-Null Count  Dtype \n",
+      "---  ------     --------------  ----- \n",
+      " 0   City Name  991 non-null    object\n",
+      " 1   Package    991 non-null    object\n",
+      " 2   Variety    991 non-null    object\n",
+      " 3   Origin     991 non-null    object\n",
+      " 4   Item Size  991 non-null    object\n",
+      " 5   Color      991 non-null    object\n",
+      "dtypes: object(6)\n",
+      "memory usage: 54.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "column_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, column_to_select]\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "pumpkins.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x21f73275010>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "palette = {\n",
+    "'ORANGE': 'orange',\n",
+    "'WHITE': 'wheat',\n",
+    "}\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y = 'Variety', hue='Color', kind='count', palette=palette\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OrdinalEncoder(categories=[['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo',\n",
+      "                            'exjbo']])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "item_size_encoder = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_featre = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_encoder)\n",
+    "print(ordinal_encoder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer([\n",
+    "    ('ord', ordinal_encoder, ordinal_featre),\n",
+    "    ('cat', categorical_encoder, categorical_features)\n",
+    "])\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['ord__Item Size', 'cat__City Name_ATLANTA', 'cat__City Name_BALTIMORE',\n",
+      "       'cat__City Name_BOSTON', 'cat__City Name_CHICAGO',\n",
+      "       'cat__City Name_COLUMBIA', 'cat__City Name_DALLAS',\n",
+      "       'cat__City Name_DETROIT', 'cat__City Name_LOS ANGELES',\n",
+      "       'cat__City Name_MIAMI', 'cat__City Name_NEW YORK',\n",
+      "       'cat__City Name_PHILADELPHIA', 'cat__City Name_SAN FRANCISCO',\n",
+      "       'cat__City Name_ST. LOUIS', 'cat__Package_1 1/9 bushel cartons',\n",
+      "       'cat__Package_1 1/9 bushel crates', 'cat__Package_1/2 bushel cartons',\n",
+      "       'cat__Package_24 inch bins', 'cat__Package_36 inch bins',\n",
+      "       'cat__Package_bins', 'cat__Package_bushel cartons',\n",
+      "       'cat__Variety_BIG MACK TYPE', 'cat__Variety_BLUE TYPE',\n",
+      "       'cat__Variety_CINDERELLA', 'cat__Variety_FAIRYTALE',\n",
+      "       'cat__Variety_HOWDEN TYPE', 'cat__Variety_HOWDEN WHITE TYPE',\n",
+      "       'cat__Variety_KNUCKLE HEAD', 'cat__Variety_MINIATURE',\n",
+      "       'cat__Variety_PIE TYPE', 'cat__Origin_ALABAMA',\n",
+      "       'cat__Origin_CALIFORNIA', 'cat__Origin_CANADA', 'cat__Origin_DELAWARE',\n",
+      "       'cat__Origin_ILLINOIS', 'cat__Origin_MARYLAND',\n",
+      "       'cat__Origin_MASSACHUSETTS', 'cat__Origin_MEXICO',\n",
+      "       'cat__Origin_MICHIGAN', 'cat__Origin_NEW JERSEY',\n",
+      "       'cat__Origin_NEW YORK', 'cat__Origin_NORTH CAROLINA',\n",
+      "       'cat__Origin_OHIO', 'cat__Origin_PENNSYLVANIA', 'cat__Origin_TENNESSEE',\n",
+      "       'cat__Origin_TEXAS', 'cat__Origin_VERMONT', 'cat__Origin_VIRGINIA',\n",
+      "       'color'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoded_pumpkins = encoded_features.assign(color=encoded_label)\n",
+    "print(encoded_pumpkins.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_19800\\1586007565.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  g = sns.catplot(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x21f78d3bed0>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pallette = {\n",
+    "     'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "g = sns.catplot(\n",
+    "    data = pumpkins,\n",
+    "    x = 'Item Size',\n",
+    "    y = 'Color',\n",
+    "    row = 'Variety',\n",
+    "    kind = 'box',\n",
+    "    orient = 'h',\n",
+    "    sharex=False,\n",
+    "    margin_titles=True,\n",
+    "    height=1.8,\n",
+    "    aspect=4,\n",
+    "    palette=pallette\n",
+    ")\n",
+    "g.set(xlabel='Item Size', ylabel='').set(xlim=(0, 6))\n",
+    "g.set_titles(row_template='{row_name}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_19800\\2511937648.py:5: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.swarmplot(x=\"color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)\n",
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 79.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n",
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\seaborn\\categorical.py:3399: UserWarning: 35.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "'0': 'orange',\n",
+    "'1': 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"color\", y=\"ord__Item Size\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "y = encoded_pumpkins['color']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00       166\n",
+      "           1       1.00      1.00      1.00        33\n",
+      "\n",
+      "    accuracy                           1.00       199\n",
+      "   macro avg       1.00      1.00      1.00       199\n",
+      "weighted avg       1.00      1.00      1.00       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1\n",
+      " 0 1 0 1 0 0 0 0 0 1 0 0 1 1]\n",
+      "F1-score:  1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[166,   0],\n",
+       "       [  0,  33]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -242,7 +659,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.13.3"
   },
   "metadata": {
    "interpreter": {
diff --git a/3-Web-App/1-Web-App/notebook.ipynb b/3-Web-App/1-Web-App/notebook.ipynb
index e69de29bb..a9e8ae778 100644
--- a/3-Web-App/1-Web-App/notebook.ipynb
+++ b/3-Web-App/1-Web-App/notebook.ipynb
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building the web Appliation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "      <th>country</th>\n",
+       "      <th>shape</th>\n",
+       "      <th>duration (seconds)</th>\n",
+       "      <th>duration (hours/min)</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>date posted</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10/10/1949 20:30</td>\n",
+       "      <td>san marcos</td>\n",
+       "      <td>tx</td>\n",
+       "      <td>us</td>\n",
+       "      <td>cylinder</td>\n",
+       "      <td>2700.0</td>\n",
+       "      <td>45 minutes</td>\n",
+       "      <td>This event took place in early fall around 194...</td>\n",
+       "      <td>4/27/2004</td>\n",
+       "      <td>29.883056</td>\n",
+       "      <td>-97.941111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10/10/1949 21:00</td>\n",
+       "      <td>lackland afb</td>\n",
+       "      <td>tx</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>light</td>\n",
+       "      <td>7200.0</td>\n",
+       "      <td>1-2 hrs</td>\n",
+       "      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n",
+       "      <td>12/16/2005</td>\n",
+       "      <td>29.384210</td>\n",
+       "      <td>-98.581082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10/10/1955 17:00</td>\n",
+       "      <td>chester (uk/england)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>gb</td>\n",
+       "      <td>circle</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>20 seconds</td>\n",
+       "      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n",
+       "      <td>1/21/2008</td>\n",
+       "      <td>53.200000</td>\n",
+       "      <td>-2.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10/10/1956 21:00</td>\n",
+       "      <td>edna</td>\n",
+       "      <td>tx</td>\n",
+       "      <td>us</td>\n",
+       "      <td>circle</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1/2 hour</td>\n",
+       "      <td>My older brother and twin sister were leaving ...</td>\n",
+       "      <td>1/17/2004</td>\n",
+       "      <td>28.978333</td>\n",
+       "      <td>-96.645833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10/10/1960 20:00</td>\n",
+       "      <td>kaneohe</td>\n",
+       "      <td>hi</td>\n",
+       "      <td>us</td>\n",
+       "      <td>light</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>15 minutes</td>\n",
+       "      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n",
+       "      <td>1/22/2004</td>\n",
+       "      <td>21.418056</td>\n",
+       "      <td>-157.803611</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "ufos = pd.read_csv('./data/ufos.csv')\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufos = pd.DataFrame({\n",
+    "    'Seconds': ufos['duration (seconds)'],\n",
+    "    'Country': ufos['country'],\n",
+    "    'Latitude': ufos['latitude'],\n",
+    "    'Longitude': ufos['longitude']\n",
+    "})\n",
+    "ufos['Country'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 25863 entries, 2 to 80330\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Seconds    25863 non-null  float64\n",
+      " 1   Country    25863 non-null  object \n",
+      " 2   Latitude   25863 non-null  float64\n",
+      " 3   Longitude  25863 non-null  float64\n",
+      "dtypes: float64(3), object(1)\n",
+      "memory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Seconds</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>53.200000</td>\n",
+       "      <td>-2.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>28.978333</td>\n",
+       "      <td>-96.645833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>35.823889</td>\n",
+       "      <td>-80.253611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>45.582778</td>\n",
+       "      <td>-122.352222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>51.783333</td>\n",
+       "      <td>-0.783333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "x = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion matrix result\n",
+    "##### [0,0] = 41: 41 samples of country 0 were correctly predicted as 0.\n",
+    "##### [1,1] = 50: 50 samples of country 1 were correctly predicted as 1.\n",
+    "##### [1,4] = 238: 238 samples of country 1 were incorrectly predicted as country 4.\n",
+    "##### [4,4] = 4686: 4686 samples of country 4 were correctly predicted as 4.\n",
+    "##### [4,1] = 14: 14 samples of country 4 were incorrectly predicted as 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       0.78      0.17      0.28       288\n",
+      "           2       1.00      0.90      0.95        10\n",
+      "           3       0.99      1.00      1.00       134\n",
+      "           4       0.95      1.00      0.97      4700\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.95      0.81      0.84      5173\n",
+      "weighted avg       0.94      0.95      0.94      5173\n",
+      "\n",
+      "Predicted labels: [4 4 4 ... 4 4 1]\n",
+      "Accuracy [4 4 4 ... 4 4 1]\n",
+      "Confusion matrix:\n",
+      " [[  41    0    0    0    0]\n",
+      " [   0   50    0    0  238]\n",
+      " [   0    0    9    1    0]\n",
+      " [   0    0    0  134    0]\n",
+      " [   0   14    0    0 4686]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(Xtrain, ytrain)\n",
+    "prediction = model.predict(Xtest)\n",
+    "print(classification_report(ytest, prediction))\n",
+    "print('Predicted labels:', prediction)\n",
+    "print('Accuracy', prediction)\n",
+    "print('Confusion matrix:\\n', confusion_matrix(ytest, prediction))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model loaded from disk [3]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufos_model.pkl'\n",
+    "pickle.dump(model, open(model_filename, 'wb'))\n",
+    "model = pickle.load(open(model_filename, 'rb'))\n",
+    "print('Model loaded from disk', model.predict([[50, 44, -12]]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/3-Web-App/1-Web-App/solution/notebook.ipynb b/3-Web-App/1-Web-App/solution/notebook.ipynb
index 430bfd9d1..054f2575e 100644
--- a/3-Web-App/1-Web-App/solution/notebook.ipynb
+++ b/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -1,46 +1,126 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
-  },
-  "metadata": {
-   "interpreter": {
-    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Build a Web App using a Regression model to learn about UFO sighting"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>city</th>\n",
+       "      <th>state</th>\n",
+       "      <th>country</th>\n",
+       "      <th>shape</th>\n",
+       "      <th>duration (seconds)</th>\n",
+       "      <th>duration (hours/min)</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>date posted</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10/10/1949 20:30</td>\n",
+       "      <td>san marcos</td>\n",
+       "      <td>tx</td>\n",
+       "      <td>us</td>\n",
+       "      <td>cylinder</td>\n",
+       "      <td>2700.0</td>\n",
+       "      <td>45 minutes</td>\n",
+       "      <td>This event took place in early fall around 194...</td>\n",
+       "      <td>4/27/2004</td>\n",
+       "      <td>29.883056</td>\n",
+       "      <td>-97.941111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10/10/1949 21:00</td>\n",
+       "      <td>lackland afb</td>\n",
+       "      <td>tx</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>light</td>\n",
+       "      <td>7200.0</td>\n",
+       "      <td>1-2 hrs</td>\n",
+       "      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n",
+       "      <td>12/16/2005</td>\n",
+       "      <td>29.384210</td>\n",
+       "      <td>-98.581082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10/10/1955 17:00</td>\n",
+       "      <td>chester (uk/england)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>gb</td>\n",
+       "      <td>circle</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>20 seconds</td>\n",
+       "      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n",
+       "      <td>1/21/2008</td>\n",
+       "      <td>53.200000</td>\n",
+       "      <td>-2.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10/10/1956 21:00</td>\n",
+       "      <td>edna</td>\n",
+       "      <td>tx</td>\n",
+       "      <td>us</td>\n",
+       "      <td>circle</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1/2 hour</td>\n",
+       "      <td>My older brother and twin sister were leaving ...</td>\n",
+       "      <td>1/17/2004</td>\n",
+       "      <td>28.978333</td>\n",
+       "      <td>-96.645833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10/10/1960 20:00</td>\n",
+       "      <td>kaneohe</td>\n",
+       "      <td>hi</td>\n",
+       "      <td>us</td>\n",
+       "      <td>light</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>15 minutes</td>\n",
+       "      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n",
+       "      <td>1/22/2004</td>\n",
+       "      <td>21.418056</td>\n",
+       "      <td>-157.803611</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
        "           datetime                  city state country     shape  \\\n",
        "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
@@ -69,11 +149,11 @@
        "2   -2.916667  \n",
        "3  -96.645833  \n",
        "4 -157.803611  "
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 23
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -86,18 +166,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "execution_count": 24
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -111,14 +191,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 25863 entries, 2 to 80330\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Seconds    25863 non-null  float64\n",
+      " 1   Country    25863 non-null  object \n",
+      " 2   Latitude   25863 non-null  float64\n",
+      " 3   Longitude  25863 non-null  float64\n",
+      "dtypes: float64(3), object(1)\n",
+      "memory usage: 1010.3+ KB\n"
      ]
     }
    ],
@@ -132,12 +222,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Seconds</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>53.200000</td>\n",
+       "      <td>-2.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>28.978333</td>\n",
+       "      <td>-96.645833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>35.823889</td>\n",
+       "      <td>-80.253611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>45.582778</td>\n",
+       "      <td>-122.352222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>51.783333</td>\n",
+       "      <td>-0.783333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
        "    Seconds  Country   Latitude   Longitude\n",
        "2      20.0        3  53.200000   -2.916667\n",
@@ -145,11 +299,11 @@
        "14     30.0        4  35.823889  -80.253611\n",
        "23     60.0        4  45.582778 -122.352222\n",
        "24      3.0        3  51.783333   -0.783333"
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "execution_count": 26
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -162,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,37 +333,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
+     "name": "stdout",
      "output_type": "stream",
-     "name": "stderr",
      "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-      "  FutureWarning)\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
-      "  \"this warning.\", FutureWarning)\n",
       "              precision    recall  f1-score   support\n",
       "\n",
       "           0       1.00      1.00      1.00        41\n",
-      "           1       1.00      0.02      0.05       250\n",
-      "           2       0.00      0.00      0.00         8\n",
-      "           3       0.94      1.00      0.97       131\n",
-      "           4       0.95      1.00      0.97      4743\n",
+      "           1       0.82      0.22      0.35       250\n",
+      "           2       1.00      1.00      1.00         8\n",
+      "           3       1.00      1.00      1.00       131\n",
+      "           4       0.96      1.00      0.98      4743\n",
       "\n",
-      "    accuracy                           0.95      5173\n",
-      "   macro avg       0.78      0.60      0.60      5173\n",
-      "weighted avg       0.95      0.95      0.93      5173\n",
+      "    accuracy                           0.96      5173\n",
+      "   macro avg       0.96      0.84      0.87      5173\n",
+      "weighted avg       0.96      0.96      0.95      5173\n",
       "\n",
       "Predicted labels:  [4 4 4 ... 3 4 4]\n",
-      "Accuracy:  0.9512855209742895\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
-      "  'precision', 'predicted', average, warn_for)\n"
+      "Accuracy:  0.9601778465107288\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
      ]
     }
    ],
    "source": [
+    "# from sklearn.model_selection import train_test_split\n",
+    "# from sklearn.metrics import accuracy_score, classification_report \n",
+    "# from sklearn.linear_model import LogisticRegression\n",
+    "# model = LogisticRegression()\n",
+    "# model.fit(X_train, y_train)\n",
+    "# predictions = model.predict(X_test)z\n",
+    "\n",
+    "# print(classification_report(y_test, predictions))\n",
+    "# print('Predicted labels: ', predictions)\n",
+    "# print('Accuracy: ', accuracy_score(y_test, predictions))\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.metrics import accuracy_score, classification_report \n",
     "from sklearn.linear_model import LogisticRegression\n",
@@ -225,20 +397,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "[3]\n"
+      "[1]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\AI\\MachineLearning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
    "source": [
     "import pickle\n",
-    "model_filename = 'ufo-model.pkl'\n",
+    "model_filename = 'ufo-model.pkl'SS\n",
     "pickle.dump(model, open(model_filename,'wb'))\n",
     "\n",
     "model = pickle.load(open('ufo-model.pkl','rb'))\n",
@@ -252,5 +432,32 @@
    "outputs": [],
    "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/3-Web-App/1-Web-App/solution/ufo-model.pkl b/3-Web-App/1-Web-App/solution/ufo-model.pkl
index 523962f2d..ead085639 100644
Binary files a/3-Web-App/1-Web-App/solution/ufo-model.pkl and b/3-Web-App/1-Web-App/solution/ufo-model.pkl differ
diff --git a/3-Web-App/1-Web-App/ufos_model.pkl b/3-Web-App/1-Web-App/ufos_model.pkl
new file mode 100644
index 000000000..b2f5f047d
Binary files /dev/null and b/3-Web-App/1-Web-App/ufos_model.pkl differ
diff --git a/4-Classification/1-Introduction/notebook.ipynb b/4-Classification/1-Introduction/notebook.ipynb
index 95cb84cd9..fd862c357 100644
--- a/4-Classification/1-Introduction/notebook.ipynb
+++ b/4-Classification/1-Introduction/notebook.ipynb
@@ -1,5 +1,742 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Delicious Asian and Indian Cuisines "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>65</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>66</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>67</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>68</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>69</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 385 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df  = pd.read_csv('../data/cuisines.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2448 entries, 0 to 2447\n",
+      "Columns: 385 entries, Unnamed: 0 to zucchini\n",
+      "dtypes: int64(384), object(1)\n",
+      "memory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='cuisine'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "thai df: (289, 385)\n",
+      "japanese df: (320, 385)\n",
+      "chinese df: (442, 385)\n",
+      "indian df: (598, 385)\n",
+      "korean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "thai_df = df[(df.cuisine == 'thai')]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "print(f'thai df: {thai_df.shape}')  \n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False,\n",
+    "    inplace=False)\n",
+    "    return ingredient_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# japanese_ingredients_df = create_ingredient_df(japanese_df)\n",
+    "# japanese_ingredients_df.head(10).plot().barh()\n",
+    "\n",
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chinese_ingredients_df = create_ingredient_df(chinese_df)\n",
+    "chinese_ingredients_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "korean_ingredients_df = create_ingredient_df(korean_df)\n",
+    "korean_ingredients_df.head(10).plot.barh()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>artemisia</th>\n",
+       "      <th>artichoke</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 380 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_df = df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis = 1)\n",
+    "labels_df = df.cuisine\n",
+    "feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_labels_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New labels shape: cuisine\n",
+      "indian      799\n",
+      "thai        799\n",
+      "chinese     799\n",
+      "japanese    799\n",
+      "korean      799\n",
+      "Name: count, dtype: int64\n",
+      "Old label Counts: cuisine\n",
+      "korean      799\n",
+      "indian      598\n",
+      "chinese     442\n",
+      "japanese    320\n",
+      "thai        289\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'New labels shape: {transformed_labels_df.value_counts()}')\n",
+    "print(f'Old label Counts: {df.cuisine.value_counts()}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3995 entries, 0 to 3994\n",
+      "Columns: 381 entries, cuisine to zucchini\n",
+      "dtypes: int64(380), object(1)\n",
+      "memory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df = pd.concat([transformed_labels_df, transformed_feature_df], axis = 1, join = 'outer')\n",
+    "transformed_df.head()\n",
+    "transformed_df.info()\n",
+    "transformed_df.to_csv(\"../data/cleaned_cuisines.csv\")"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,19 +747,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.13.3"
   },
   "orig_nbformat": 2
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "source": [
-    "# Delicious Asian and Indian Cuisines "
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}
diff --git a/4-Classification/2-Classifiers-1/notebook.ipynb b/4-Classification/2-Classifiers-1/notebook.ipynb
index 30778dee9..342f51c8d 100644
--- a/4-Classification/2-Classifiers-1/notebook.ipynb
+++ b/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -1,5 +1,657 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build Classification Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## predict a national cuisine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 382 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_curve, precision_score\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>artemisia</th>\n",
+       "      <th>artichoke</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 380 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cusisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cusisines_features_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cusisines_features_df, cuisines_label_df, test_size=0.3, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic Regression Accuracy: 0.79\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/workspaces/ML-For-Beginners/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class = 'ovr', solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print(f\"Logistic Regression Accuracy: {accuracy:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ingredients: Index(['fish', 'lime_juice', 'shallot'], dtype='object')\n",
+      "cuisine: thai\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/workspaces/ML-For-Beginners/.venv/lib/python3.11/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>thai</th>\n",
+       "      <td>0.839568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>japanese</th>\n",
+       "      <td>0.134624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chinese</th>\n",
+       "      <td>0.014531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>korean</th>\n",
+       "      <td>0.008383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>indian</th>\n",
+       "      <td>0.002894</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 0\n",
+       "thai      0.839568\n",
+       "japanese  0.134624\n",
+       "chinese   0.014531\n",
+       "korean    0.008383\n",
+       "indian    0.002894"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.69      0.69      0.69       236\n",
+      "      indian       0.91      0.91      0.91       245\n",
+      "    japanese       0.73      0.72      0.73       231\n",
+      "      korean       0.81      0.76      0.78       242\n",
+      "        thai       0.78      0.84      0.81       245\n",
+      "\n",
+      "    accuracy                           0.79      1199\n",
+      "   macro avg       0.79      0.78      0.78      1199\n",
+      "weighted avg       0.79      0.79      0.79      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,19 +662,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.11.4"
   },
   "orig_nbformat": 2
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "source": [
-    "# Build Classification Models"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}
diff --git a/4-Classification/3-Classifiers-2/notebook.ipynb b/4-Classification/3-Classifiers-2/notebook.ipynb
index 4659a7b62..bc0becd76 100644
--- a/4-Classification/3-Classifiers-2/notebook.ipynb
+++ b/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -4,17 +4,184 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Build Classification Model"
+    "# Classification Model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 382 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
        "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
        "0           0  indian       0         0      0           0      0   \n",
@@ -38,11 +205,11 @@
        "4                        0     0     0    0      0       1         0  \n",
        "\n",
        "[5 rows x 382 columns]"
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+      ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 9
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -53,11 +220,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "0    indian\n",
@@ -68,8 +234,9 @@
        "Name: cuisine, dtype: object"
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -79,12 +246,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>artemisia</th>\n",
+       "      <th>artichoke</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 380 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
        "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
        "0       0         0      0           0      0             0        0   \n",
@@ -108,26 +442,156 @@
        "4                        0     0     0    0      0       1         0  \n",
        "\n",
        "[5 rows x 380 columns]"
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+      ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
     "cuisines_feature_df.head()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = 10\n",
+    "#create different classifiers\n",
+    "classifiers = {\n",
+    "    'Linear SVC' : SVC(kernel='linear', C=c, probability=True, random_state=0),\n",
+    "    'KNN Classifier' : KNeighborsClassifier(c),\n",
+    "    'SVC' : SVC(kernel='rbf', C = 10, gamma=\"scale\"), #Support Vector Classifier\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100), #Emsamble Cllasifier\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuraccy (train) of Linear SVC: 81.0%\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.75      0.73       235\n",
+      "      indian       0.90      0.88      0.89       254\n",
+      "    japanese       0.83      0.76      0.79       236\n",
+      "      korean       0.86      0.77      0.82       247\n",
+      "        thai       0.76      0.89      0.82       227\n",
+      "\n",
+      "    accuracy                           0.81      1199\n",
+      "   macro avg       0.81      0.81      0.81      1199\n",
+      "weighted avg       0.81      0.81      0.81      1199\n",
+      "\n",
+      "Accuraccy (train) of KNN Classifier: 74.4%\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.67      0.72      0.70       235\n",
+      "      indian       0.81      0.77      0.79       254\n",
+      "    japanese       0.68      0.81      0.74       236\n",
+      "      korean       0.92      0.57      0.71       247\n",
+      "        thai       0.72      0.85      0.78       227\n",
+      "\n",
+      "    accuracy                           0.74      1199\n",
+      "   macro avg       0.76      0.75      0.74      1199\n",
+      "weighted avg       0.76      0.74      0.74      1199\n",
+      "\n",
+      "Accuraccy (train) of SVC: 84.1%\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.80      0.80       235\n",
+      "      indian       0.90      0.91      0.90       254\n",
+      "    japanese       0.90      0.77      0.83       236\n",
+      "      korean       0.87      0.81      0.83       247\n",
+      "        thai       0.76      0.93      0.84       227\n",
+      "\n",
+      "    accuracy                           0.84      1199\n",
+      "   macro avg       0.84      0.84      0.84      1199\n",
+      "weighted avg       0.85      0.84      0.84      1199\n",
+      "\n",
+      "Accuraccy (train) of RFST: 85.9%\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.83      0.81      0.82       235\n",
+      "      indian       0.92      0.91      0.92       254\n",
+      "    japanese       0.88      0.81      0.85       236\n",
+      "      korean       0.88      0.83      0.85       247\n",
+      "        thai       0.79      0.93      0.86       227\n",
+      "\n",
+      "    accuracy                           0.86      1199\n",
+      "   macro avg       0.86      0.86      0.86      1199\n",
+      "weighted avg       0.86      0.86      0.86      1199\n",
+      "\n",
+      "Accuraccy (train) of ADA: 69.7%\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.63      0.61      0.62       235\n",
+      "      indian       0.87      0.79      0.83       254\n",
+      "    japanese       0.52      0.83      0.64       236\n",
+      "      korean       0.84      0.64      0.72       247\n",
+      "        thai       0.79      0.62      0.69       227\n",
+      "\n",
+      "    accuracy                           0.70      1199\n",
+      "   macro avg       0.73      0.70      0.70      1199\n",
+      "weighted avg       0.73      0.70      0.70      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(x_train, np.ravel(y_train))\n",
+    "    y_pred = classifier.predict(x_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuraccy (train) of %s: %0.1f%%\" %(name, accuracy * 100))\n",
+    "    print(classification_report(y_test, y_pred))\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -139,7 +603,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.13.3"
   },
   "metadata": {
    "interpreter": {
@@ -149,4 +613,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/4-Classification/4-Applied/CouisineMatcher.js b/4-Classification/4-Applied/CouisineMatcher.js
new file mode 100644
index 000000000..7f03bed4a
--- /dev/null
+++ b/4-Classification/4-Applied/CouisineMatcher.js
@@ -0,0 +1,43 @@
+// Test alert message to verify JavaScript is working
+alert('JavaScript is working! CuisineMatcher.js loaded successfully.');
+
+const ingredients = Array(380).fill(0);
+    
+const checks = [...document.querySelectorAll('.checkbox')];
+
+checks.forEach(check => {
+    check.addEventListener('change', function() {
+        ingredients[check.value] = check.checked ? 1 : 0;
+    });
+});
+
+function testCheckboxes() {
+    return checks.some(check => check.checked);
+}
+
+async function startInference() {
+
+    let atLeastOneChecked = testCheckboxes()
+
+    if (!atLeastOneChecked) {
+        alert('Please select at least one ingredient.');
+        return;
+    }
+    try {
+        
+        const session = await ort.InferenceSession.create('./model.onnx');
+
+        const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
+        const feeds = { float_input: input };
+
+        // feed inputs and run
+        const results = await session.run(feeds);
+
+        // read from results
+        alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
+
+    } catch (e) {
+        console.log(`failed to inference ONNX model`);
+        console.error(e);
+    }
+}
\ No newline at end of file
diff --git a/4-Classification/4-Applied/index.html b/4-Classification/4-Applied/index.html
new file mode 100644
index 000000000..b9b8726ec
--- /dev/null
+++ b/4-Classification/4-Applied/index.html
@@ -0,0 +1,65 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <title>Cuisine Matcher</title>
+        <link rel="stylesheet" href="styles.css">
+        <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.9.0/dist/ort.min.js"></script>
+    </head>
+    <body>
+       
+        <nav>
+            <div class="logo">
+                <a href="#">Cuisine Matcher</a>
+            </div>
+            <div class="nav-links">
+                <a href="#">Home</a>
+                <a href="#">Recipes</a>
+                <a href="#">About</a>
+                <a href="#">Contact</a>
+            </div>
+        </nav>
+
+        <h1 style="padding: 20px 20px 0 20px;">Check your refrigerator. What can you create?</h1>
+
+        <div id="wrapper">
+            <div class="boxCont">
+                <input type="checkbox" value="4" class="checkbox">
+                <label>apple</label>
+            </div>
+        
+            <div class="boxCont">
+                <input type="checkbox" value="247" class="checkbox">
+                <label>pear</label>
+            </div>
+        
+            <div class="boxCont">
+                <input type="checkbox" value="77" class="checkbox">
+                <label>cherry</label>
+            </div>
+
+            <div class="boxCont">
+                <input type="checkbox" value="126" class="checkbox">
+                <label>fenugreek</label>
+            </div>
+
+            <div class="boxCont">
+                <input type="checkbox" value="302" class="checkbox">
+                <label>sake</label>
+            </div>
+
+            <div class="boxCont">
+                <input type="checkbox" value="327" class="checkbox">
+                <label>soy sauce</label>
+            </div>
+
+            <div class="boxCont">
+                <input type="checkbox" value="112" class="checkbox">
+                <label>cumin</label>
+            </div>
+        </div>
+        <div style="padding: 20px">
+            <button onClick="startInference()">What kind of cuisine can you make?</button>
+        </div> 
+        <script src="CouisineMatcher.js"></script>
+    </body>
+</html>
diff --git a/4-Classification/4-Applied/model.onnx b/4-Classification/4-Applied/model.onnx
new file mode 100644
index 000000000..4b27b87f2
Binary files /dev/null and b/4-Classification/4-Applied/model.onnx differ
diff --git a/4-Classification/4-Applied/notebook.ipynb b/4-Classification/4-Applied/notebook.ipynb
index a1f5a65c3..f339d4699 100644
--- a/4-Classification/4-Applied/notebook.ipynb
+++ b/4-Classification/4-Applied/notebook.ipynb
@@ -1,5 +1,1084 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build a cuisine recommender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 25.0.1 -> 25.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: skl2onnx in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (1.19.1)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from skl2onnx) (1.19.0)\n",
+      "Requirement already satisfied: scikit-learn>=1.1 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from skl2onnx) (1.6.1)\n",
+      "Requirement already satisfied: numpy>=1.22 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from onnx>=1.2.1->skl2onnx) (2.2.6)\n",
+      "Requirement already satisfied: protobuf>=4.25.1 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from onnx>=1.2.1->skl2onnx) (6.32.1)\n",
+      "Requirement already satisfied: typing_extensions>=4.7.1 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from onnx>=1.2.1->skl2onnx) (4.14.0)\n",
+      "Requirement already satisfied: ml_dtypes in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from onnx>=1.2.1->skl2onnx) (0.5.3)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from scikit-learn>=1.1->skl2onnx) (1.15.3)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from scikit-learn>=1.1->skl2onnx) (1.5.1)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in d:\\ai\\machinelearning\\ml-for-beginners\\.venv\\lib\\site-packages (from scikit-learn>=1.1->skl2onnx) (3.6.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install skl2onnx\n",
+    "import pandas as pd "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 382 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the column names in the dataset\n",
+    "print(\"Column names:\")\n",
+    "print(data.columns.tolist())\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "data.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>artemisia</th>\n",
+       "      <th>artichoke</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 380 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = data.iloc[:, 2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cuisine</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>indian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>indian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>indian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>indian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>indian</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]   \n",
+    "y.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score, precision_score, confusion_matrix, classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, kernel=&#x27;linear&#x27;, probability=True, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=10, kernel=&#x27;linear&#x27;, probability=True, random_state=0)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
+    "model = SVC(kernel='linear', C=10, probability=True, random_state=0)\n",
+    "model.fit(X_train, y_train.values.ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.73      0.74      0.74       244\n",
+      "      indian       0.87      0.87      0.87       240\n",
+      "    japanese       0.76      0.75      0.75       241\n",
+      "      korean       0.84      0.78      0.81       235\n",
+      "        thai       0.77      0.84      0.80       239\n",
+      "\n",
+      "    accuracy                           0.79      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.79      0.79      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onx = convert_sklearn(model, initial_types=initial_type, options= options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,19 +1089,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": 3
+   "version": "3.13.3"
   },
   "orig_nbformat": 4
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "source": [
-    "# Build a cuisine recommender"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}
diff --git a/4-Classification/4-Applied/styles.css b/4-Classification/4-Applied/styles.css
new file mode 100644
index 000000000..3dacfd4f3
--- /dev/null
+++ b/4-Classification/4-Applied/styles.css
@@ -0,0 +1,40 @@
+/* Basic styling for navigation */
+body {
+    font-family: Arial, sans-serif;
+    margin: 0;
+    padding: 0;
+}
+
+nav {
+    background-color: #4CAF50;
+    padding: 10px 20px;
+    color: white;
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+nav a {
+    color: white;
+    text-decoration: none;
+    margin: 0 10px;
+    font-weight: bold;
+}
+
+nav a:hover {
+    text-decoration: underline;
+}
+
+#wrapper {
+    padding: 20px;
+}
+
+.boxCont {
+    margin-bottom: 10px;
+}
+
+button {
+    padding: 10px 20px;
+    font-size: 16px;
+    cursor: pointer;
+}
diff --git a/4-Classification/data/cleaned_cuisines.csv b/4-Classification/data/cleaned_cuisines.csv
index 84526cc22..1410429ae 100644
--- a/4-Classification/data/cleaned_cuisines.csv
+++ b/4-Classification/data/cleaned_cuisines.csv
@@ -2447,1550 +2447,1550 @@
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diff --git a/5-Clustering/1-Visualize/notebook.ipynb b/5-Clustering/1-Visualize/notebook.ipynb
index 46c72bc1c..7df098074 100644
--- a/5-Clustering/1-Visualize/notebook.ipynb
+++ b/5-Clustering/1-Visualize/notebook.ipynb
@@ -1,5 +1,721 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nigerian Music scraped from Spotify - an analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: seaborn in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (0.13.2)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from seaborn) (2.3.5)\n",
+      "Requirement already satisfied: pandas>=1.2 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from seaborn) (2.3.3)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from seaborn) (3.10.7)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.61.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\n",
+      "Requirement already satisfied: pyparsing>=3 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\desktop\\ml-for-beginners\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 25.0.1 -> 25.3\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top Generes')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index, y = top[:5].values)\n",
+    "plt.xticks(rotation =45 )\n",
+    "plt.title('Top Generes', color='blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr(numeric_only=True)\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "g=sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind= \"kde\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x24248e70590>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 651.25x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", height=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,30 +726,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.13.3"
   },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
-   "display_name": "Python 3.8.3 64-bit (conda)"
-  }
+  "orig_nbformat": 2
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "source": [
-    "# Nigerian Music scraped from Spotify - an analysis"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}
diff --git a/5-Clustering/2-K-Means/notebook.ipynb b/5-Clustering/2-K-Means/notebook.ipynb
index b25d4f188..f7d541b67 100644
--- a/5-Clustering/2-K-Means/notebook.ipynb
+++ b/5-Clustering/2-K-Means/notebook.ipynb
@@ -1,84 +1,172 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
-  },
-  "metadata": {
-   "interpreter": {
-    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Nigerian Music scraped from Spotify - an analysis"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
-      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
-      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
-      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
-      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
-      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
-      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
-      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
-      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
-      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pip install seaborn"
+    "# %pip install seaborn"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Start where we finished in the last lesson, with data imported and filtered."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "0                    Sparky            Mandy & The Jungle   \n",
@@ -107,11 +195,11 @@
        "2       0.0424  130.005               4  \n",
        "3       0.1130  111.087               4  \n",
        "4       0.0447  105.115               4  "
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      ]
      },
+     "execution_count": 1,
      "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -126,37 +214,36 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We will focus only on 3 genres. Maybe we can get 3 clusters built!"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0.5, 1.0, 'Top genres')"
       ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 7
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
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3gAMmUpQkSdJU5M7rkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUyc2VfmGQT4MsDTQ8F3g6sBbwEGOvb31pVJ610hZIkSVPESgerqroQ2BwgyQxgIXA88ELgw1X1gSYVSpIkTRGthgK3By6uqssaXU+SJGnKaRWs9gSOGXh+YJJzkhyVZO3xXpBkXpL5SeaPjY2Nd4okSdKUMuFgleQewC7AV/qmw4GH0Q0TLgI+ON7rquqIqppbVXNnzZo10TIkSZKGrkWP1dOBs6rqKoCquqqqbquq24EjgS0bvIckSdLIaxGs9mJgGDDJBgPHdgfOa/AekiRJI2+lVwUCJLk3sAPw0oHm9yXZHCjg0qWOSZIkTVsTClZV9Sfg/ku17TOhiiRJkqYod16XJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKmRmcMuQNJd95vDHj3sEjTNPOjt5w67BGlasMdKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWpk5kQvkORS4AbgNmBxVc1Nsg7wZWA2cCmwR1X9bqLvJUmSNMpa9Vg9uao2r6q5/fM3A6dU1RzglP65JEnStDZZQ4G7Akf3j48Gdpuk95EkSRoZLYJVAd9NcmaSeX3belW1qH98JbDe0i9KMi/J/CTzx8bGGpQhSZI0XBOeYwU8saoWJnkAcHKSXw4erKpKUku/qKqOAI4AmDt37h2OS5IkTTUT7rGqqoX996uB44EtgauSbADQf796ou8jSZI06iYUrJLcO8l9ljwGngacB5wI7Nufti9wwkTeR5IkaSqY6FDgesDxSZZc64tV9e0kZwDHJtkfuAzYY4LvI0mSNPImFKyq6hLgseO0XwtsP5FrS5IkTTXuvC5JktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIysdrJJsnOQHSS5Icn6SV/Xt70iyMMnZ/dfO7cqVJEkaXTMn8NrFwOuq6qwk9wHOTHJyf+zDVfWBiZcnSZI0dax0sKqqRcCi/vENSX4BbNiqMEmSpKmmyRyrJLOBxwE/7ZsOTHJOkqOSrL2M18xLMj/J/LGxsRZlSJIkDdWEg1WSNYHjgFdX1R+Aw4GHAZvT9Wh9cLzXVdURVTW3qubOmjVromVIkiQN3YSCVZLV6ULVF6rqawBVdVVV3VZVtwNHAltOvExJkqTRN5FVgQE+C/yiqj400L7BwGm7A+etfHmSJElTx0RWBW4N7AOcm+Tsvu2twF5JNgcKuBR46YQqlCRJmiImsirwx0DGOXTSypcjSZI0dbnzuiRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUyke0WJEmaNFt/bOthl6Bp5rRXnjbp72GPlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUyacEqyU5JLkyyIMmbJ+t9JEmSRsWkBKskM4BPAE8HNgP2SrLZZLyXJEnSqJisHqstgQVVdUlV3QJ8Cdh1kt5LkiRpJKSq2l80+Rdgp6p6cf98H2Crqjpw4Jx5wLz+6SbAhc0L0Z1ZF7hm2EVIk8yfc60K/Dm/+z24qmaNd2Dm3V3JElV1BHDEsN5/VZdkflXNHXYd0mTy51yrAn/OR8tkDQUuBDYeeL5R3yZJkjRtTVawOgOYk+QhSe4B7AmcOEnvJUmSNBImZSiwqhYnORD4DjADOKqqzp+M99JKcxhWqwJ/zrUq8Od8hEzK5HVJkqRVkTuvS5IkNWKwkiRJasRgpSaSzE1yn2HXIUnSMBms1MpLgO8ariRp6kmSYdcwXRisNCFJtgCoqpcCZwLHG640VYz3x8Q/MFrVJElVVZKtk+yfZPt+qyStBFcFakKSnA7cWFVP6Z8fDswBdq+qG4ZanLQCkmxDt6HxH4Bv9n9gVquq24dcmnS3SfJk4LPAl4FnAEcDX6+qBUMtbAqyx0oTUlWPB2Yk+Ub//OXARdhzpRG2pFcqyVzgKGBrYG/g60tClT1XWlUk2QR4GfDqqnoLsC/dB+QdhlrYFGWw0l028EdpJkBVbQvMWipc/RL4fpI1h1aotAx9r9T2wFuAF1fVK4D9gKuBjyw5Z3gVSpMvPWAb4GHAjknuXVVnAccA85KsPdQipyCDle6SJWPx/dMNk8yBv/Rc3T/JN/vnBwKnAusMp1JpudYCdgf+sX9+C/BpwLklmtYGemPXBWZW1ZHAu4DQ3YIO4Erghr5Nd4FzrLRSkrwO2Bm4J/D9qjq4bz8VoKq2GWJ50h0MTNBdD7ihqm5M8s/A14Gdq+rkJDsA76MbArnWXitNV0l2Bg4DFgJ/AvYHnk03DLga3S3v3l9V3xxakVPUpNwrUNNbkhcBu1TVtkk+Brw2yd9V1euqapsk30mycVVdPuxapSX6UPVM4JVAJTmNrodqN+A7SY6l+4R+WFVdM8RSpUmV5JHAO4EDgbOBLwL/r6r2TPJnYEfg3CWhaqmRCi2HQ4FarnEm8S4A9knySmBD4DHA3kk+BVBVOxqqNGqSPIyuN+oNwAfoQtShwLfohgSfCfxPVR2/ZP6gNE3dDFwAnFVVN1bVbsAGSQ6g68H9KfDYJHsaqu46f3louZb8o+onot9cVacmuR+wLfC+qrq4/7S/VZJ1quq6YdYrDRr4w7A2cFlV/W/f/htgK+CpVXVCkn2BY5P8uqp+OLyKpbYGhsFn0HWoXAdsAMwFftyf9iW6X/eLkxwN3Ar8wFB119ljpWVK8rAkm/WPXwt8nm45+gOq6nrg18Czk7yZrufq2YYqjYqBntZ79d/PAxYnORCgqi4ELgc2659/FfgXYNHdXKo0qfpQtStwLN0+VY8EPgF8LMmBSV5MNyy4oD//1qo6uqquGlrRU5iT1zWuJPcCPgZcRddlPA94Od2ta3YHtqALU7sBTwYOqqrzhlOtNL4kO9H9zF4CnA4U3Z5Va9J9Qv80sF9V/Y9DHpqukmwKfAb4d7qVgO8A9qHrldoR2Aj4alV9d1g1TicGKy1Tv5XCa4H7AudX1bv79g8DOwFPqqprktyzqv48xFKlO0jyeOC9dB8QHkO3jcKtdJ/aX0230/r3q+obQytSmmRJHgV8ELiwqg7q23YEPkf3O9yd1RtzKFB/Y3CielVdBLwbuB54TJLH9O2vAf4b+EE/Zn/LMGqVliXJhnQT1H/aD/G9D/gh3bySRVW1P/CGqvqGO6xrmvsV3Z5Uj0wyJ8kaVfUd4Dhg1nBLm54MVvqLwaGQJM9NshuwKV2v1fXA7gPhah7dpN/bvKeaRtBNdJNy90yyVVX9saq+DTyIrveKqlrcf7fbXtNSkhlVdQvwYrq5g68HdkmyLfAsYPEw65uuDFb6i4FQdSDdXj8A36D7Q/ReYH26bRb+vj929d1epDSOgdssPSrJdnRzqN5D11N1WJKn90PbGwO/H1qh0t2k/6B8W5KZVXUrXbhaDfhXulC1X1WdYY9tewYr/UWS1ZJsQDcZfXvgocApwM+r6hK6YcGZdBPa/aSvkdGvetoZOAF4Id1ePM+kG/47jW4DxE8AL6qqs/xjoulm4MPFnCTrL2nvt0+Y2fdcvQKYD/wdcJYLNiaHwWoVt9QfmBl0+5tcS7cr7zbAc6rq1iQv7895vbtSa9QkuTfdH419qmpfuo0/twXWo/tZPhj4I93PtzStDOxTtSNwIt0HiwOSPBz+JlzdSvfv5AF0NyB3L8tJYLBahS01p2pvYF5V3Uy3JP0gun2pbkzyPLr7SFVV3Ta8iqW/SrJa//0f6XaSvgbYBKCqTqDbt+oN/enH0n1SPyTJPe/+aqXJ04equXTDfc8EXgf8PbDbUuFqyZyr5wAf7IOWGjOtrsIGQtUBwIvo9jWhql6aZC3g1CQ/p9uder+qumJoxUq9JPeqqpuq6vYkTwQOp7tx7M+AjZPMrar5dCtXtwBmVNXVSY4AbndrEE03Se5DNwS+Rb99woL+g8dewHOTfKWqftXPuVqtD1e/HWbN05n7WK3ikqwNHAG8qaou6Zfi3twf24muJ+DSqvr1MOuU4C978vwH8Ay6rRMOp9vY8DNJHgocQLfIYjHwD8DBVXX8sOqVJsvS86OSbAJ8lG739Ff2Hzy2A54PvNvf4Xcfg9UqZrzJikm+Rrf673MDvVhbAedU1U1DKFO6gySr0wWpn9L9vD6NbthjbeAFVfXbJOvS7SK9KbCgquY7QVfTzcCcqh3othBJ/+HiEcCb6Ta/fW0frtauqt8NteBVjHOsViFLzama03/CAfgO8GDgn/pjzwXeRrdkXRolC+kmpX+Fbs7UYcDZwEFJ1q+qa6rq7Kr6Uj8c6OpVTSv9UF4l+We6HdWvoNtS5ANV9Su61dvrAx/vX3L9kEpdZdljtYpYKlS9lm5O1U3AfwH/RnfvqMfRDaE8DHheVZ07nGqlv7XUJ/T/BH5YVXv2x7amGxq8F92Qh/uradpJ8hBgtaq6uO+Z/U/gNcCSXqoNge9V1f79h+Y1quqc4VW86nLy+ipiIFQ9HngC8ERgDeAMYHFVHdzvYfVwuiGURUMrVhowEKoeSncLjmcBr07yTrqVTaf1E3V3oxsWNFhpOnoCcFGSK/p7tM6j+3k/lG4+4Wy6Ses3VdWBQ6xzlWewWoUkeSRwCF2v1GpVdVW/VP0nSR5YVa+gu+2BNDL6ULUL3bDfAuAS4NN0S8oPSvLRqvrvJOdWlbuqa1qqqi8kWRM4I8neVXVOkgcCZ/ZzqdYHPkQ3tUND5ByraWzp3aWr6hfAkXTBarsk61bVVcDWwBOTrOeO1Bo1fS/rwcCOwPF0Gxw+je4my9sCr+s3PzRUadoZ2FF9R+BRdEOAR/YrZC8F7pfkk3Q3VT6hqk729/hwOcdqmhpn88916O5y/h3g2XTDJl8HTu33+Jnh5p8aRUk2ottaYW26XdSfB3yKbhf1zwFjVXXG0AqUJlmSLYGPAK+pqtP7ebLPo/s9Dt39XP9UVT8aVo36K4cCp6mBUPUaYFe6VVRvotvs893AbcB+wK1JvgHcPpxKpTvXb0x7RZJ3AV+oqgVJPk93d4Dzq+qy4VYoTZ4kGwNvBM6tqtMBqupDfafUyXS3HTtpiCVqKQaraaa/fcE6VfWzfk7VFnQ3VX4D3f/f69NtpXAY3ZDgmVVlqNJUcC7w0n4/q2cBrzJUaRWwGDgH2DXJTlX1bfhLuJoBrDXU6nQHBqtppL+twf7A6kluBf6Xbhnu04Gdge3ptlk4iO7WHocOq1ZpJZxEt5J1F+BdVXXakOuRmh/RBFIAAAVUSURBVBtYBftPdKtgf0N3t4HfA7snubWqTgGoqvcPsVQtg5PXp4l+07gb6HamXgzsCTyiqhYC9wN+1t8f6hbgW3SrqqQpo6r+UFVHA8+tqv9ygq6moz5UPQ04ClgPOJNugdGJdD1X+/XHNaLssZomBobzdgQeC2wC3DPJZ4CfAJ/t96naDtihqq4cSqHSxN0G7qiu6affj20t4KXA7nSLji4Azuq3x/kKXa+t2+KMMFcFTiNJngR8DNgSeDywE7A63XyqNek2kbugqi4ZWpGSpDuV5E10Iw1PAZ7f77a+H3AqcKnzYkebQ4HTy5rAtVV1S1WdSrevyVPo7hm1TlV901AlSaMnyeZJDumf3hvYB9i7D1WPpVvV/UBD1ehzKHB6+RmwMMmewFeq6swkp9EF6KuGW5okadDARPUnAc8BdkxydVW9PcmmwCFJFgObA2+qqh8PtWCtEIPV9HI98GO6vaqelmQ+3T0Bn11V1wy1MkkS8NdA1YeqbYAvAAcCC4EnJ1mjqvZI8kS6jXE/3n9QjnMLR59zrKaYfvXfMruCk9wL2JRu4uOawGer6vy7qz5J0rL19/d7JPDDqrqtvzPGhlX13v5egJsD7wWOraqPDLNWrRyD1RTV/2N8EHADcMx4PVL9/dMW3+3FSZLGlWRX4CLgCrqtcbYHPky3WvvX/crAo+nmWX21qr44tGK1Upy8PgUleRHdxp8X093376Akj+6PZcn+PoYqSRotVXUCcCXwSbp7/X2X7t6XH+nnVT2G7t6YFwEbDqtOrTznWE0B44yrbwe8vqq+neRU4GC6DUHPdfxdkkbP4O/xqrouyY+Ap9Ft2nw8EOA/6Xqx9qe7HdkO/S2cFvu7fepwKHAKGFg58jLgDLpb1NwT+FD/D/QhdLv07l5Vvx9mrZKk8SXZFng08P2quiDJXnS/z79eVV9Lcu/+1C3p7o6xu3Nkpx6HAkdYkk3gL7c4eBawB/BbunC1Ft3Kv7WAR9F9yrllWLVKku5oydSMJFvRDf9tC7wxyUuq6hjgm8DeSfYA/kz3ofkJwK6GqqnJocARlWRH4PAkW9CNt78YOK+qFgGLkmwMbNO33wN4ZVXdOLSCJUl30H8w3hI4FNirqs7p9xp8Qh+ujkwyA7iwqm4Drk3y/v7erpqCDFYjKMlMuq7gg4HN6Jbf/gDYNckz+h3UP5PkfnR7nPypqsaGV7Ek6U6sBTwV2IHuRspfBW6nn0NVVZ+Ev9nfylA1hRmsRlBVLU5yMfA2uhvOPpmui/gmYJcki6vq21V1Pd2moJKkEVVV3+2nc/x7kt9W1TFJvgrMAP534DwnPU8DBqvRdQ5wI/AH4H5VdU2Sr9F9ytk3ya1VdcpQK5QkrZCqOrG/Pc2/JblHVR0NHDPsutSeqwJHxOBS3CT3AG7rd+V9Pd2NlA+pqjOSbES3iuSb/XwrSdIUkWQX4D10Q4NXelPl6cdgNQKWClUH0s2r+gPwjqr6c5K30t3/7z1V9ZMkM/pJjpKkKSbJLOfFTl8GqxGS5BXAc4HnAWcB3wPeXlUXJ3kn8HBgv6r68xDLlCRJy2CwGhFJ7gt8iG4l4HOAnYGr6bZaeHlVLUhy/6q6dohlSpKkO2GwGiFJ1gA2Bf6jqp7cbyw3RrcD7zuq6tahFihJku6UqwJHSFXdnORGYGZ/U+UHA6cAnzZUSZI0+uyxGjF9r9Wr6VaMPBB4TlVdMNyqJEnSijBYjaD+bubrA7dX1cJh1yNJklaMwUqSJKmR1YZdgCRJ0nRhsJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmN/B/Djeb5PsBsCgAAAABJRU5ErkJggg==\n"
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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
      },
-     "metadata": {
-      "needs_background": "light"
-     }
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -171,12 +258,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Kasala</td>\n",
+       "      <td>Pioneers</td>\n",
+       "      <td>DRB Lasgidi</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>184800</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.803</td>\n",
+       "      <td>0.1270</td>\n",
+       "      <td>0.525</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.1290</td>\n",
+       "      <td>-10.034</td>\n",
+       "      <td>0.1970</td>\n",
+       "      <td>100.103</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Pull Up</td>\n",
+       "      <td>Everything Pretty</td>\n",
+       "      <td>prettyboydo</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>202648</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.4520</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>0.004490</td>\n",
+       "      <td>0.5900</td>\n",
+       "      <td>-9.840</td>\n",
+       "      <td>0.1990</td>\n",
+       "      <td>95.842</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
@@ -205,16 +428,277 @@
        "4       0.0447  105.115               4  \n",
        "5       0.1970  100.103               4  \n",
        "6       0.1990   95.842               4  "
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "execution_count": 8
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "df.head()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='release_date'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 4000x4000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LabelEncoder  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      2\u001b[39m le = LabelEncoder()\n\u001b[32m      3\u001b[39m x = df.loc[:, (\u001b[33m'\u001b[39m\u001b[33martist_top_genre\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mpopularity\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mdanceability\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33macousticness\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mloudness\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33menergy\u001b[39m\u001b[33m'\u001b[39m)]\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder  # pyright: ignore[reportMissingImports]\n",
+    "le = LabelEncoder()\n",
+    "x = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "y = df['artist_top_genre']\n",
+    "x['artist_top_genre'] = le.fit_transform(x['artist_top_genre'])  # pyright: ignore[reportUndefinedVariable]\n",
+    "y = le.transform(y)  # pyright: ignore[reportUndefinedVariable]\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split  # pyright: ignore[reportMissingImports]\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier  # pyright: ignore[reportMissingImports]\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=42)  # pyright: ignore[reportUndefinedVariable]\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(x_test)\n",
+    "\n",
+    "from sklearn.metrics import classification_report, confusion_matrix  # pyright: ignore[reportMissingImports]\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcluster\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KMeans  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      3\u001b[39m nclusters = \u001b[32m3\u001b[39m \n\u001b[32m      4\u001b[39m seed = \u001b[32m0\u001b[39m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans  # pyright: ignore[reportMissingImports]\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "X = x.values  # pyright: ignore[reportUndefinedVariable]\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)  # pyright: ignore[reportUndefinedVariable]\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)  # pyright: ignore[reportUndefinedVariable]\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m metrics  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      2\u001b[39m score = metrics.silhouette_score(X, y_cluster_kmeans)\n\u001b[32m      3\u001b[39m score\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics  # pyright: ignore[reportMissingImports]\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcluster\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KMeans  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      2\u001b[39m wcss = []\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m1\u001b[39m, \u001b[32m11\u001b[39m):\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans  # pyright: ignore[reportMissingImports]\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(x=range(1, 11), y=wcss, marker='o', color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans  # pyright: ignore[reportMissingImports]\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/6-NLP/1-Introduction-to-NLP/solution/bot.py b/6-NLP/1-Introduction-to-NLP/solution/bot.py
index ca36fcbc1..f452bc5b4 100644
--- a/6-NLP/1-Introduction-to-NLP/solution/bot.py
+++ b/6-NLP/1-Introduction-to-NLP/solution/bot.py
@@ -1,26 +1,91 @@
 import random
+import string
+# New Update chatbot code with intent detection and state management
+INTENTS = {
+   "greeting": ["hello", "hi", "hey"],
+    "question": ["why", "what", "how", "is", "are", "?"],
+    "opinion": ["i think", "i believe", "in my opinion"],
+    "goodbye": ["bye", "exit", "quit"]
+}
+responses ={
+     "greeting": [
+        "Hello. How can I assist you today?"
+    ],
+    "question": [
+        "That is a thoughtful question. Could you clarify further?",
+        "Let us explore that in more detail."
+    ],
+    "opinion": [
+        "That is an interesting perspective. What led you to that conclusion?"
+    ],
+    "unknown": [
+        "I am not entirely sure I understand. Could you rephrase?",
+        "Could you provide more detail so I can respond appropriately?"
+    ]
+}
+conversation_state = {
+    "last_intent": None,
+    "last_topic": None
+}
+def normalize(text):
+    text = text.lower()
+    return text.translate(str.maketrans('', '', string.punctuation))
+def detect_intent(text):
+    for intent, patterns in INTENTS.items():
+        for pattern in patterns:
+            if pattern in text:
+                return intent
+    return "unknown"
 
-# This list contains the random responses (you can add your own or translate them into your own language too)
-random_responses = ["That is quite interesting, please tell me more.",
-                    "I see. Do go on.",
-                    "Why do you say that?",
-                    "Funny weather we've been having, isn't it?",
-                    "Let's change the subject.",
-                    "Did you catch the game last night?"]
-
-print("Hello, I am Marvin, the simple robot.")
-print("You can end this conversation at any time by typing 'bye'")
-print("After typing each answer, press 'enter'")
-print("How are you today?")
+def extract_topic(text):
+    keywords = ["coding", "programming", "money", "career", "finance"]
+    for word in keywords:
+        if word in text:
+            return word
+    return None
 
+def generate_response(intent, topic):
+    base_response = random.choice(responses.get(intent, responses["unknown"]))
+    if topic:
+        return f"{base_response} You mentioned {topic}, Would you like to discuss it further?"
+    return base_response
+print("Hello, I am Paskal Chatbot, the conversational AI.")
+print("Type 'bye' to end the conversation.")
 while True:
-    # wait for the user to enter some text
     user_input = input("> ")
-    if user_input.lower() == "bye":
-        # if they typed in 'bye' (or even BYE, ByE, byE etc.), break out of the loop
+    normalized_input = normalize(user_input)
+    if normalized_input == "bye":
+        print("It was nice talking to you, goodbye!")
         break
-    else:
-        response = random.choices(random_responses)[0]
+    intent = detect_intent(normalized_input)
+    topic = extract_topic(normalized_input)
+    conversation_state["last_intent"] = intent
+    if topic:
+        conversation_state["last_topic"] = topic
+    response = generate_response(intent, topic)
     print(response)
 
-print("It was nice talking to you, goodbye!")
+    # This list contains the random responses (you can add your own or translate them into your own language too)
+# random_responses = ["That is quite interesting, please tell me more.",
+#                     "I see. Do go on.",
+#                     "Why do you say that?",
+#                     "Funny weather we've been having, isn't it?",
+#                     "Let's change the subject.",
+#                     "Did you catch the game last night?"]
+
+# print("Hello, I am Marvin, the simple robot.")
+# print("You can end this conversation at any time by typing 'bye'")
+# print("After typing each answer, press 'enter'")
+# print("How are you today?")
+
+# while True:
+#     # wait for the user to enter some text
+#     user_input = input("> ")
+#     if user_input.lower() == "bye":
+#         # if they typed in 'bye' (or even BYE, ByE, byE etc.), break out of the loop
+#         break
+#     else:
+#         response = random.choices(random_responses)[0]
+#     print(response)
+
+# print("It was nice talking to you, goodbye!")
diff --git a/6-NLP/2-Tasks/solution/assignment.py b/6-NLP/2-Tasks/solution/assignment.py
new file mode 100644
index 000000000..266a82a74
--- /dev/null
+++ b/6-NLP/2-Tasks/solution/assignment.py
@@ -0,0 +1,40 @@
+from textblob import TextBlob
+from textblob.np_extractors import ConllExtractor
+extracctor = ConllExtractor();
+
+
+def mani():
+    print("Hello, I am Paskal, the friendly robot")
+    print("You can end the conversation at any time typing  'bye'")
+    print("After typing each sentence presthe Enter for the sentiment analysis")
+    print("How are you feeling today?")
+
+    while True:
+        user_input = input("You:")
+        if user_input.lower() in ('bye', "exit", "quit"):
+            break
+
+        user_blob = TextBlob(user_input, np_extractor=extracctor)
+        polarity = user_blob.polarity
+        noun_phrases = user_blob.noun_phrases
+
+        if polarity <= -0.5:
+            response = "Oh Dear, that sounds really bad!"
+        elif polarity <= 0:
+            response = "Hmm, that's not great."
+        elif polarity < 0.5:
+            response = "Thats sound positive! "
+        else:
+            response = "Yay! That sounds awesome!"
+        if noun_phrases:
+            np = noun_phrases[0]
+            try:
+                plural_np = np.pluralize()
+            except Exception:
+                plural_np = np
+            response += f"Can you tell me more about {plural_np}?"
+        else:
+            response += "Can you tell me more about that?"
+        print(response)
+    print("Goodbye! It was nice talking to you.")
+mani()