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 00000000..c41c9f4d
--- /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
+116,4/26/2011,2,0,4,0,2,1,1,0.631667,0.594083,0.729167,0.3265,678
+117,4/27/2011,2,0,4,0,3,1,2,0.62,0.575142,0.835417,0.3122,547
+118,4/28/2011,2,0,4,0,4,1,2,0.6175,0.578929,0.700833,0.320908,569
+119,4/29/2011,2,0,4,0,5,1,1,0.51,0.497463,0.457083,0.240063,878
+120,4/30/2011,2,0,4,0,6,0,1,0.4725,0.464021,0.503333,0.235075,1965
+121,5/1/2011,2,0,5,0,0,0,2,0.451667,0.448204,0.762083,0.106354,1138
+122,5/2/2011,2,0,5,0,1,1,2,0.549167,0.532833,0.73,0.183454,847
+123,5/3/2011,2,0,5,0,2,1,2,0.616667,0.582079,0.697083,0.342667,603
+124,5/4/2011,2,0,5,0,3,1,2,0.414167,0.40465,0.737083,0.328996,255
+125,5/5/2011,2,0,5,0,4,1,1,0.459167,0.441917,0.444167,0.295392,614
+126,5/6/2011,2,0,5,0,5,1,1,0.479167,0.474117,0.59,0.228246,894
+127,5/7/2011,2,0,5,0,6,0,1,0.52,0.512621,0.54125,0.16045,1612
+128,5/8/2011,2,0,5,0,0,0,1,0.528333,0.518933,0.631667,0.0746375,1401
+129,5/9/2011,2,0,5,0,1,1,1,0.5325,0.525246,0.58875,0.176,664
+130,5/10/2011,2,0,5,0,2,1,1,0.5325,0.522721,0.489167,0.115671,694
+131,5/11/2011,2,0,5,0,3,1,1,0.5425,0.5284,0.632917,0.120642,550
+132,5/12/2011,2,0,5,0,4,1,1,0.535,0.523363,0.7475,0.189667,695
+133,5/13/2011,2,0,5,0,5,1,2,0.5125,0.4943,0.863333,0.179725,692
+134,5/14/2011,2,0,5,0,6,0,2,0.520833,0.500629,0.9225,0.13495,902
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+147,5/27/2011,2,0,5,0,5,1,1,0.681667,0.637008,0.65375,0.240679,871
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+149,5/29/2011,2,0,5,0,0,0,1,0.6675,0.61555,0.81875,0.213938,2355
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+155,6/4/2011,2,0,6,0,6,0,1,0.635,0.594696,0.45625,0.123142,1869
+156,6/5/2011,2,0,6,0,0,0,2,0.648333,0.616804,0.6525,0.138692,1685
+157,6/6/2011,2,0,6,0,1,1,1,0.678333,0.621858,0.6,0.121896,673
+158,6/7/2011,2,0,6,0,2,1,1,0.7075,0.65595,0.597917,0.187808,763
+159,6/8/2011,2,0,6,0,3,1,1,0.775833,0.727279,0.622083,0.136817,676
+160,6/9/2011,2,0,6,0,4,1,2,0.808333,0.757579,0.568333,0.149883,563
+161,6/10/2011,2,0,6,0,5,1,1,0.755,0.703292,0.605,0.140554,815
+162,6/11/2011,2,0,6,0,6,0,1,0.725,0.678038,0.654583,0.15485,1729
+163,6/12/2011,2,0,6,0,0,0,1,0.6925,0.643325,0.747917,0.163567,1467
+164,6/13/2011,2,0,6,0,1,1,1,0.635,0.601654,0.494583,0.30535,863
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+166,6/15/2011,2,0,6,0,3,1,1,0.626667,0.587754,0.471667,0.167912,769
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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
+710,12/10/2012,4,1,12,0,1,1,2,0.435833,0.435575,0.925,0.190308,329
+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 e69de29b..8a71517b 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 c9b9925b..ca45c5b1 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 2902cce8..e3cd14ce 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 7c212763..923c74b5 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 e69de29b..a9e8ae77 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 430bfd9d..054f2575 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 523962f2..ead08563 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 00000000..b2f5f047
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 95cb84cd..fd862c35 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 30778dee..342f51c8 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 4659a7b6..bc0becd7 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 00000000..f7c837c6
--- /dev/null
+++ b/4-Classification/4-Applied/CouisineMatcher.js
@@ -0,0 +1,47 @@
+// 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() {
+        // toggle the state of the ingredient
+        // based on the checkbox's value (1 or 0)
+        ingredients[check.value] = check.checked ? 1 : 0;
+    });
+});
+
+function testCheckboxes() {
+    // validate if at least one checkbox is checked
+    return checks.some(check => check.checked);
+}
+
+async function startInference() {
+
+    let atLeastOneChecked = testCheckboxes()
+
+    if (!atLeastOneChecked) {
+        alert('Please select at least one ingredient.');
+        return;
+    }
+    try {
+        // create a new session and load the model.
+        
+        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 00000000..3e508122
--- /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>
+        <!-- Navigation Bar -->
+        <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 00000000..4b27b87f
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 a1f5a65c..f339d469 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",
+       "      <td>0</td>\n",
+       "      <td>...</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",
+       "      <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",
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+       "      <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",
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+       "      <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",
+       "      <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 00000000..3dacfd4f
--- /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 84526cc2..1410429a 100644
--- a/4-Classification/data/cleaned_cuisines.csv
+++ b/4-Classification/data/cleaned_cuisines.csv
@@ -2447,1550 +2447,1550 @@
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