diff --git a/.gitignore b/.gitignore
index f780d576..58459341 100644
--- a/.gitignore
+++ b/.gitignore
@@ -360,4 +360,5 @@ MigrationBackup/
 .Rdata
 .Rhistory
 ML-For-Beginners.Rproj
-
+.venv
+.obsidian
\ No newline at end of file
diff --git a/2-Regression/1-Tools/assignment.ipynb b/2-Regression/1-Tools/assignment.ipynb
new file mode 100644
index 00000000..f9bf94d1
--- /dev/null
+++ b/2-Regression/1-Tools/assignment.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#libraries\n",
+    "from sklearn import datasets, model_selection, linear_model\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load the dataset\n",
+    "linnerud = datasets.load_linnerud()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  ['Chins', 'Situps', 'Jumps']\n",
+      "Target:  ['Weight', 'Waist', 'Pulse']\n",
+      "[[162.]\n",
+      " [110.]\n",
+      " [101.]\n",
+      " [105.]\n",
+      " [155.]\n",
+      " [101.]\n",
+      " [101.]\n",
+      " [125.]\n",
+      " [200.]\n",
+      " [251.]\n",
+      " [120.]\n",
+      " [210.]\n",
+      " [215.]\n",
+      " [ 50.]\n",
+      " [ 70.]\n",
+      " [210.]\n",
+      " [ 60.]\n",
+      " [230.]\n",
+      " [225.]\n",
+      " [110.]] [36. 37. 38. 35. 35. 36. 38. 34. 31. 33. 34. 33. 34. 46. 36. 37. 37. 32.\n",
+      " 33. 33.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# select the feature and target variables\n",
+    "print('Features: ', linnerud.feature_names)\n",
+    "print('Target: ', linnerud.target_names)\n",
+    "\n",
+    "waistline = linnerud.data[:,1].reshape(-1,)\n",
+    "situps = linnerud.target[:, 1]\n",
+    "\n",
+    "print(waistline, situps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = situps\n",
+    "y = waistline\n",
+    "\n",
+    "# splitting the dataset into training and testing splits\n",
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X,y, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Expected 2D array, got 1D array instead:\narray=[34. 36. 37. 38. 36. 34. 38. 31. 36. 33. 32. 33. 34. 35.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\2-Regression\\1-Tools\\assignment.ipynb Cell 5\u001b[0m line \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/DEV%20WORK/Data%20Science%20Library/ML-For-Beginners/2-Regression/1-Tools/assignment.ipynb#W4sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m model \u001b[39m=\u001b[39m linear_model\u001b[39m.\u001b[39mLinearRegression()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/DEV%20WORK/Data%20Science%20Library/ML-For-Beginners/2-Regression/1-Tools/assignment.ipynb#W4sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\base.py:1151\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1144\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[0;32m   1146\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[0;32m   1147\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[0;32m   1148\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1149\u001b[0m     )\n\u001b[0;32m   1150\u001b[0m ):\n\u001b[1;32m-> 1151\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_base.py:678\u001b[0m, in \u001b[0;36mLinearRegression.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    674\u001b[0m n_jobs_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs\n\u001b[0;32m    676\u001b[0m accept_sparse \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpositive \u001b[39melse\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mcsr\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcsc\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcoo\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m--> 678\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m    679\u001b[0m     X, y, accept_sparse\u001b[39m=\u001b[39;49maccept_sparse, y_numeric\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, multi_output\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[0;32m    680\u001b[0m )\n\u001b[0;32m    682\u001b[0m has_sw \u001b[39m=\u001b[39m sample_weight \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    683\u001b[0m \u001b[39mif\u001b[39;00m has_sw:\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\base.py:621\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    619\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[0;32m    620\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 621\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n\u001b[0;32m    622\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[0;32m    624\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:1147\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1142\u001b[0m         estimator_name \u001b[39m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1143\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m   1144\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m requires y to be passed, but the target y is None\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1145\u001b[0m     )\n\u001b[1;32m-> 1147\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[0;32m   1148\u001b[0m     X,\n\u001b[0;32m   1149\u001b[0m     accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[0;32m   1150\u001b[0m     accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[0;32m   1151\u001b[0m     dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[0;32m   1152\u001b[0m     order\u001b[39m=\u001b[39;49morder,\n\u001b[0;32m   1153\u001b[0m     copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[0;32m   1154\u001b[0m     force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[0;32m   1155\u001b[0m     ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[0;32m   1156\u001b[0m     allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[0;32m   1157\u001b[0m     ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[0;32m   1158\u001b[0m     ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[0;32m   1159\u001b[0m     estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[0;32m   1160\u001b[0m     input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m   1161\u001b[0m )\n\u001b[0;32m   1163\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[0;32m   1165\u001b[0m check_consistent_length(X, y)\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:940\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    938\u001b[0m     \u001b[39m# If input is 1D raise error\u001b[39;00m\n\u001b[0;32m    939\u001b[0m     \u001b[39mif\u001b[39;00m array\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 940\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    941\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mExpected 2D array, got 1D array instead:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39marray=\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m    942\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    943\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    944\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mif it contains a single sample.\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[0;32m    945\u001b[0m         )\n\u001b[0;32m    947\u001b[0m \u001b[39mif\u001b[39;00m dtype_numeric \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(array\u001b[39m.\u001b[39mdtype, \u001b[39m\"\u001b[39m\u001b[39mkind\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m array\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39min\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mUSV\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m    948\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    949\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mdtype=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnumeric\u001b[39m\u001b[39m'\u001b[39m\u001b[39m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    950\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    951\u001b[0m     )\n",
+      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[34. 36. 37. 38. 36. 34. 38. 31. 36. 33. 32. 33. 34. 35.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
+     ]
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import necessary libraries\n",
+    "from sklearn import datasets\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load the linnerud dataset\n",
+    "linnerud = datasets.load_linnerud()\n",
+    "\n",
+    "# The Linnerud dataset contains physiological and exercise variables measured on 20 middle-aged men in a fitness club.\n",
+    "# The exercise variables are: Weight, Waist and Pulse.\n",
+    "# The physiological variables are: Chins, Situps and Jumps.\n",
+    "\n",
+    "# Let's assume 'Waist' is at index 1 and 'Situps' is at index 1 in the data\n",
+    "waistline = linnerud.data[:, 1].reshape(-1, 1)\n",
+    "situps = linnerud.target[:, 1]\n",
+    "\n",
+    "# Create a Linear Regression model\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# Fit the model with waistline and situps data\n",
+    "model.fit(waistline, situps)\n",
+    "\n",
+    "# Now we can plot the relationship between waistline and situps\n",
+    "plt.scatter(waistline, situps, color='blue')\n",
+    "plt.plot(waistline, model.predict(waistline), color='red')\n",
+    "plt.title('Relationship between Waistline and Situps')\n",
+    "plt.xlabel('Waistline')\n",
+    "plt.ylabel('Situps')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import necessary libraries\n",
+    "from sklearn import datasets\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load the Linnerud dataset\n",
+    "linnerud = datasets.load_linnerud()\n",
+    "\n",
+    "# The Linnerud dataset contains physiological and exercise variables measured on 20 middle-aged men in a fitness club.\n",
+    "# The exercise variables are: Weight, Waist and Pulse.\n",
+    "# The physiological variables are: Chins, Situps and Jumps.\n",
+    "\n",
+    "# Create a Linear Regression model\n",
+    "model = LinearRegression()\n",
+    "\n",
+    "# For each pair of exercise and physiological variables\n",
+    "for i in range(3):\n",
+    "    for j in range(3):\n",
+    "        # Extract the data for the current pair of variables\n",
+    "        x = linnerud.data[:, i].reshape(-1, 1)\n",
+    "        y = linnerud.target[:, j]\n",
+    "\n",
+    "        # Fit the model with the current pair of data\n",
+    "        model.fit(x, y)\n",
+    "\n",
+    "        # Plot the relationship between the current pair of variables\n",
+    "        plt.figure(figsize=(8, 6))\n",
+    "        plt.scatter(x, y, color='blue')\n",
+    "        plt.plot(x, model.predict(x), color='red')\n",
+    "        plt.title(f'Relationship between {linnerud.feature_names[i]} and {linnerud.target_names[j]}')\n",
+    "        plt.xlabel(linnerud.feature_names[i])\n",
+    "        plt.ylabel(linnerud.target_names[j])\n",
+    "        plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index 5f46b45f..3de3a076 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,12 +744,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn import datasets,"
+    "linnerud = datasets.load_linnerud()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features:  ['Chins', 'Situps', 'Jumps']\n"
+     ]
+    }
+   ],
+   "source": [
+    "features = print(\"Features: \", linnerud.feature_names)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Labels:  ['Weight', 'Waist', 'Pulse']\n"
+     ]
+    }
+   ],
+   "source": [
+    "target = print(\"Labels: \", linnerud.target_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Found input variables with inconsistent numbers of samples: [14, 6]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\2-Regression\\1-Tools\\notebook.ipynb Cell 15\u001b[0m line \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/DEV%20WORK/Data%20Science%20Library/ML-For-Beginners/2-Regression/1-Tools/notebook.ipynb#X40sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m model \u001b[39m=\u001b[39m linear_model\u001b[39m.\u001b[39mLinearRegression()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/DEV%20WORK/Data%20Science%20Library/ML-For-Beginners/2-Regression/1-Tools/notebook.ipynb#X40sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train,y_train)\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\base.py:1151\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1144\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[0;32m   1146\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[0;32m   1147\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[0;32m   1148\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1149\u001b[0m     )\n\u001b[0;32m   1150\u001b[0m ):\n\u001b[1;32m-> 1151\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_base.py:678\u001b[0m, in \u001b[0;36mLinearRegression.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    674\u001b[0m n_jobs_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs\n\u001b[0;32m    676\u001b[0m accept_sparse \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpositive \u001b[39melse\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mcsr\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcsc\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mcoo\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m--> 678\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[0;32m    679\u001b[0m     X, y, accept_sparse\u001b[39m=\u001b[39;49maccept_sparse, y_numeric\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, multi_output\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m\n\u001b[0;32m    680\u001b[0m )\n\u001b[0;32m    682\u001b[0m has_sw \u001b[39m=\u001b[39m sample_weight \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    683\u001b[0m \u001b[39mif\u001b[39;00m has_sw:\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\base.py:621\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    619\u001b[0m         y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[0;32m    620\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 621\u001b[0m         X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n\u001b[0;32m    622\u001b[0m     out \u001b[39m=\u001b[39m X, y\n\u001b[0;32m    624\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:1165\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1147\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[0;32m   1148\u001b[0m     X,\n\u001b[0;32m   1149\u001b[0m     accept_sparse\u001b[39m=\u001b[39maccept_sparse,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1160\u001b[0m     input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   1161\u001b[0m )\n\u001b[0;32m   1163\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[1;32m-> 1165\u001b[0m check_consistent_length(X, y)\n\u001b[0;32m   1167\u001b[0m \u001b[39mreturn\u001b[39;00m X, y\n",
+      "File \u001b[1;32md:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\utils\\validation.py:409\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    407\u001b[0m uniques \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(lengths)\n\u001b[0;32m    408\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(uniques) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 409\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    410\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m    411\u001b[0m         \u001b[39m%\u001b[39m [\u001b[39mint\u001b[39m(l) \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m lengths]\n\u001b[0;32m    412\u001b[0m     )\n",
+      "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [14, 6]"
+     ]
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/2-Regression/2-Data/notebook.ipynb b/2-Regression/2-Data/notebook.ipynb
index c9b9925b..318ae92a 100644
--- a/2-Regression/2-Data/notebook.ipynb
+++ b/2-Regression/2-Data/notebook.ipynb
@@ -1,5 +1,565 @@
 {
+ "cells": [
+  {
+   "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>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>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</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>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</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>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</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>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</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>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</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  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case = True, regex= True)]\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_to_select = ['Package','Low Price','High Price','Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'], 'High Price': pumpkins['High Price'],'Price': price})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price   Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  15.000\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  18.000\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  18.000\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  17.000\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  15.000\n",
+      "...     ...                   ...        ...         ...     ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  15.000\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  14.375\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  12.875\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  12.000\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  12.000\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 +1/9)\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Month</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>1 1/9 bushel cartons</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.00</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.00</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.00</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1/2 bushel cartons</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>30.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1/2 bushel cartons</td>\n",
+       "      <td>13.75</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>28.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1/2 bushel cartons</td>\n",
+       "      <td>10.75</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1/2 bushel cartons</td>\n",
+       "      <td>12.00</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1/2 bushel cartons</td>\n",
+       "      <td>12.00</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Month               Package  Low Price  High Price  Price\n",
+       "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+       "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+       "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+       "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+       "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+       "...     ...                   ...        ...         ...    ...\n",
+       "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+       "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+       "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+       "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+       "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+       "\n",
+       "[415 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_pumpkins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "9import matplotlib.pyplot as plt\n",
+    "\n",
+    "price = x = new_pumpkins.Price\n",
+    "month = y = new_pumpkins.Month\n",
+    "\n",
+    "## Scatter plot\n",
+    "plt.scatter(price,month)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 11,
+     "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(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,24 +570,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.10.11"
   },
-  "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..cd87d6c3 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\\jnopa\\AppData\\Local\\Temp\\ipykernel_7552\\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 0x28ba23b6050>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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uuOMO/PrXvwYALF26FB999BE2btwIs9mM5cuXQ6/X47PPPhP1vlxqS90pKq/C6p0VXne4tZqNyM9JZ5MxDbjpZ7vQ0s3d4wwROvzzpVkhyIikuv2VfTh3pWvBmJoQjeInp4QgI1KboC+1ra+vx/z581FYWIjrrrvOs91ut+Ptt9/Gr371K0yZMgXjxo3Dhg0b8Ne//hWHDh0K5J8iQlF5FZZuPuZVeABAtd2JpZuPoai8KkSZkRi+Cg8AaGkXcNPPdimcEUnlq/AAgHNXmnD7K/sUzoi0LqDiY9myZZg9ezamTZvmtf3o0aNobW312j5ixAikpKSgtLS0d5lSn9TuErB6ZwW6++pyb1u9s4KXYFTqq9omn4WHW0u7wEswKmZvbPVZeLidu9LESzAkieTiY+vWrTh27BgKCgq67KuurobBYMCAAQO8tg8aNAjV1dXdvl9zczMcDofXg8jtsK22y4jHtQQAVXYnDttqlUuKRLvr1WJZ40h5D288LGscESCx+Lhw4QIef/xxvPvuuzAajT2/QISCggKYzWbPIzk5WZb3pfBQU+e78AgkjpTV0Nwuaxwp75Kf4j+QOCJAYvFx9OhR1NTUYOzYsYiMjERkZCSKi4vx6quvIjIyEoMGDUJLSwuuXr3q9brLly/DYrF0+555eXmw2+2ex4ULFwI+GAo/iXHiilyxcaSs/lERssaR8pLM4j5bYuOIAInFx9SpU3Hy5EmUlZV5Ht/5zncwf/58z3/369cPe/fu9bymsrIS58+fR1ZWVrfvGRUVBZPJ5PUgcstMi4fVbISve9fq0LHqJTMtXsm0SKSP/+t2WeNIeb9/KFPWOCIAiJQSHBcXh4yMDK9t/fv3R0JCgmf74sWLsWrVKsTHx8NkMuGxxx5DVlYWbr31Vvmypj4jQq9Dfk46lm4+Bh3gNfHUXZDk56QjQu+rPKFQuiE+GoYInd9Jp4YIHW6Ij1YwK5LCHNMPqQnRfiedpiZEwxzTT8GsSOtk73C6bt06fO9738PcuXMxadIkWCwWbNu2Te5/hvqQmRlWrH9wLCydhnUtZiPWPziWfT5U7p8vzYIhovvikH0+tKH4ySlITei+QGSfDwpEwE3GgoVNxsiXdpeAw7Za1NQ5kRjXcamFIx7a8VVtE+56tRgNze3oHxWBj//rdo54aIy9sRUPbzyMS3YnksxG/P6hTI54kIeU728WH0RERNRrQe9wSkRERBQoFh9ERESkKBYfREREpCgWH0RERKQoSX0+tIwrJbTva0czvv/mQdQ2tCK+fz9s//FEXG+KCnVaJFJLmwubSs/iXG0jUuNjsCBrCAyR/PuHqC/qE6tdisqrsHpnhdcNyqxmI/Jz0tkjQiNGPb8bDmdbl+0mYyROPD8jBBmRFAW7KlBYYsO1Nx/W64Dc7DTkzUoPXWJEJBuudrlGUXkVlm4+1uXOqNV2J5ZuPoai8qoQZUZi+So8AMDhbMOo53crnBFJUbCrAm8d8C48AMAlAG8dsKFgV0VoEiOikAnr4qPdJWD1zgp0N7Tj3rZ6ZwXaO/9WJNX42tHss/Bwczjb8LWjWaGMSIqWNhcKS2x+YwpLbGhpcymUERGpQVgXH4dttV1GPK4lAKiyO3HYVqtcUiTJ9988KGscKWtT6dkuIx6duYSOOCLqO8K6+Kip8114BBJHyqttaJU1jpR1rrZR1jgiCg9hXXwkxhl7DpIQR8qL7y/uvhFi40hZqfExssYRUXgI6+IjMy0eVrMRvhbU6tCx6iUzLV7JtEiC7T+eKGscKWtB1hD0tKJdr+uII6K+I6yLjwi9Dvk5Hcv4Ov/+cz/Pz0lnvw8Vu94UBZPRfzsakzGS/T5UyhCpR252mt+Y3Ow09vsg6mPC/hM/M8OK9Q+OhcXsfWnFYjZi/YNj2edDA048P8NnAcI+H+qXNysdj0xK6zICotcBj0xinw+ivqhPNBkD2OE0HLDDqbaxwylReJPy/d1nig8iIiIKHnY4JSIiItXqMzeWI6LQ4qVPInJj8UFEQcebOxLRtXjZhYiCijd3JKLOWHwQUdDw5o5E1B1ediHNOPrlN5j7u796nv9lyW0YN/S6EGZEPZFyc8esYQnKJUYB4bwdkguLD9KEIc981GWbuxA5u2a20umQSLy5Y/jgvB2SEy+7kOp1V3hI2U+hw5s7hgfO2yG5sfggVTv65TeyxpGyhFZxcznExpHyOG+HgoHFB6natXM85IgjZT2w8XNZ40h5UubtEInF4oOIiHzivB0KBhYfRETkE+ftUDCw+CBV+8uS22SNI2VtffhWWeNIeZlp8bCajfC1oFaHjlUvmWnxSqZFGsfig1RNbB8P9vtQp1tvEte7Q2wcKS9Cr0N+TjoAdClA3M/zc9LZ74MkkVR8rF+/HqNGjYLJZILJZEJWVhY+/vhjz/7JkydDp9N5PR599FHZk6a+pac+HuzzoW48f9o3M8OKaemJXVa8CACmpSeyzwdJJqn4GDx4MNasWYOjR4/ib3/7G6ZMmYK7774bf//73z0xubm5qKqq8jxefvll2ZOmviX3nSO92k+hxT4t2lewqwJ7Kmq63benogYFuyoUzoi0TlLxkZOTg1mzZuFb3/oWbrrpJrz00kuIjY3FoUOHPDExMTGwWCyeh8lkkj1p6juaWtp9/tJz21NRg6aWdoUyIikqL9XJGkfKa2lzobDE5jemsMSGljaXQhlROAh4zkd7ezu2bt2KhoYGZGVleba/++67GDhwIDIyMpCXl4fGxka/79Pc3AyHw+H1IHL7pci/qMTGkbJmvXZA1jhS3qbSs+ipf5hL6IgjEkvyvV1OnjyJrKwsOJ1OxMbGYvv27UhP75iM9MADDyA1NRVJSUk4ceIEnn76aVRWVmLbtm0+36+goACrV68O/AgorJ294r94lRpHymoX2fRSbBwp71ytuM+W2DgiIIDiY/jw4SgrK4Pdbsf//M//YNGiRSguLkZ6ejqWLFniiRs5ciSsViumTp2KM2fOYNiwYd2+X15eHlatWuV57nA4kJycHMChUDgakhCDklPi4kh9InTiCosILpRQrdR4cZ8tsXFEQACXXQwGA2688UaMGzcOBQUFGD16NH7zm990GzthwgQAwOnTp32+X1RUlGf1jPtB5PbTWemyxpGydj02SdY4Ut6CrCHoaRWtXtcRRyRWr/t8uFwuNDc3d7uvrKwMAGC1chkWBSbaEIHp6Yl+Y6anJyLaEKFQRiTF8KQ4WeNIeYZIPXKz0/zG5GanwRDJtlEknqSflry8PBw4cABnz57FyZMnkZeXh/3792P+/Pk4c+YMXnjhBRw9ehRnz57Fjh07sHDhQkyaNAmjRo0KVv7UBxQuHN+r/RRa7POhfXk9jCz2tJ+oM0nFR01NDRYuXIjhw4dj6tSpOHLkCHbv3o3p06fDYDDgk08+wZ133okRI0bgiSeewNy5c7Fz585g5U59xNA8/30getpPocU+H9rHzyDJTdKE07ffftvnvuTkZBQXF/c6IaJr2WoaRC3zs9U0IC2xvzJJkWgnz9tFx41MMQc5GwoEP4MUDLxIR6o28zfiClqxcaSsOW8elDWOlMfPIAUDiw9StWaRDSDExpGyxJ4Vnj314meQgoHFB6lalMgGEGLjSFlizwrPnnrxM0jBwOKDVK3o8dtljSNl7fjxRFnjSHn8DFIwsPggVUtL7C+qwREnuqmT2EmknGyqXvwMUjCw+CDV+7Jgts9ffnpdx35SL/b50D5+BkluLD5IExL6GyRtJyJ5JcdHS9pO5A+LD1K98S/uwdf1Ld3u+7q+BeNf3KNwRiQFm4xp3+2v7MO5K03d7jt3pQm3v7JP4YxI61h8kKrV1rf4LDzcvq5vQW0PMRQaH35+QdY4Up69sdVn4eF27koT7I2tCmVE4YDFB6nafb/7q6xxpKzl20/IGkfKe3jjYVnjiAAWH6RyNXXiRjTExhGRNJfsTlnjiAAWH6RyiXHiJpSKjSMiaZLMRlnjiAAWH6RyW5fcJmscKev174+SNY6U9/uHMmWNIwJYfJDKxccacH2s/1GN62MNiO8hhkLjexOSZY0j5Zlj+iE1wf9y2tSEaJhj+imUEYUDFh+kekd+Ph3R/br/UY3up8eRn09XOCOSgk3GtK/4ySk+C5DUhGgUPzlF4YxI61h8kOoV7KpAU6ur231NrS4U7KpQOCOSoqi8yueN43T/3k/qV/zkFPzfc3diXMoAWM1GjEsZgP977k4WHhQQnSAIqroPssPhgNlsht1uh8lkCnU6FGItbS6MePZjuPz8lOp1wD9euAuGSNbSatPuEjBx7T5U+VgJoQNgMRtx8OkpiOjpBiJEpGpSvr/525pUbVPpWb+FBwC4hI44Up/DtlqfhQcACACq7E4cttUqlxQRhRyLD1K1c7WNssaRsmrqxPV+EBtHROGBxQepWmp8jKxxpKzEOHG9H8TGEVF4YPFBqvbAhFRZ40hZmWnxsJqNfiecWs1GZKbFK5kWEYUYiw9StbILV2WNI2VF6HXIz0n3G5Ofk87JphrR0ubC2yVf4rkPyvF2yZdoaet+FRpRTyJDnQCRP5wzoH0zM6yIM0bC4Wzrsi/OGImZGdYQZEVSFeyqQGGJzWsC+Eu7vkBudhryZvkvMIk648gHqRrnDGjf+Bf3dFt4AIDD2YbxL+5ROCOSqmBXBd46YOuy8swlAG8dsLHXDknG4oNU7dvJA2SNI2XV1rfg63r/dxz+ur4FtT3EUOi0tLlQWGLzG1NYYuMlGJKExQep2pbPz8kaR8q673d/lTWOlMdeOxQMLD5I1c78q17WOFJWTZ24EQ2xcaQ89tqhYGDxQar2taNZ1jhSVmKcuLsNi40j5bHXDgUDiw9StUEmcRNJxcaRsrYuuU3WOFLegqwh6GkltF7XEUckFosPUrW0gf1ljSNlxccacH2s/1GN62MNiO8hhkLHEKlHbnaa35jc7DTe2JEkkfTTsn79eowaNQomkwkmkwlZWVn4+OOPPfudTieWLVuGhIQExMbGYu7cubh8+bLsSVPfwb+6tO/Iz6f3aj+FXt6sdDwyKa3LZ1GvAx6ZxD4fJJ1OEIQe5jH/x86dOxEREYFvfetbEAQBf/jDH/DKK6/g+PHjuOWWW7B06VJ89NFH2LhxI8xmM5YvXw69Xo/PPvtMdEJSbslLfcOQZz7qMebsmtkKZEKB4PkLHy1tLmwqPYtztY1IjY/BgqwhHPEgDynf35I6nObk5Hg9f+mll7B+/XocOnQIgwcPxttvv40tW7ZgypQpAIANGzbg5ptvxqFDh3DrrbdKPAwi4GDF16LjJqZfH+RsSKqT5+2i40ammIOcDfWWIVKPxdlDQ50GhYGAS9b29nZs3boVDQ0NyMrKwtGjR9Ha2opp06Z5YkaMGIGUlBSUlpb6fJ/m5mY4HA6vB5Hbg+8cljWOlDXnzYOyxhFReJBcfJw8eRKxsbGIiorCo48+iu3btyM9PR3V1dUwGAwYMGCAV/ygQYNQXV3t8/0KCgpgNps9j+TkZMkHQUTqJPaaruhrv0QUFiQXH8OHD0dZWRk+//xzLF26FIsWLUJFReB9/fPy8mC32z2PCxcuBPxeRKQuYu9Vy3vaEvUtkosPg8GAG2+8EePGjUNBQQFGjx6N3/zmN7BYLGhpacHVq1e94i9fvgyLxeLz/aKiojyrZ9wPIrfNCzNljSNl7fjxRFnjiCg89HqassvlQnNzM8aNG4d+/fph7969nn2VlZU4f/48srKyevvPUB8ldhIpJ5uqk9hJpJxsStS3SCo+8vLycODAAZw9exYnT55EXl4e9u/fj/nz58NsNmPx4sVYtWoVPv30Uxw9ehQ//OEPkZWVxZUu1Cs9LcPkMk114/kjos4kLbWtqanBwoULUVVVBbPZjFGjRmH37t2YPr2jSdC6deug1+sxd+5cNDc3Y8aMGXjzzTeDkjj1LWfXzMbBiq+9VrVsXpjJEQ+NOLtmNk6et2POmwchoGOOx44fT+SIB1EfJanJmBLYZIx8YYMjIiL1ClqTMaJQKdhVgcISG1zXlMov7foCudls7UxEpDUsPkj1CnZV4K0Dti7bXQI821mAEBFpB8esSdVa2lwoLOlaeFyrsMSGljaXQhkREVFvsfggVdtUetbrUkt3XEJHHBERaQOLD1K1c7WNssYREVHocc4HqVpqfIyscRQ65//ViJm/KUZTqwvR/fQoevx2pAzkeSPqi7jUllStpc2FEc9+7PfSi14H/OOFu7jsVsVu/OlH6G5aTqQeOP1LNhkjCgdSvr/525pUzRCpR8YN/n+IM24wsfBQMV+FBwC0uTr2E1Hfwt/YpGotbS6Uf+XwG1P+lYOrXVTq/L8afRYebm2ujjgi6jtYfJCqcbWLts38TbGscUQUHlh8kKpxtYu2NbWKG5ESG0dE4YHFB6kaV7toW3Q/cb9ixMYRUXjgJ55Ubd74FFnjSFlFj98uaxwRhQcWH6Rq7x05L2scKStlYAx6WogUqQf7fRD1MSw+SNU450P7Tv9yts8ChH0+iPomdjglVeOcj/Bw+pez2eGUiDzY4ZRUjR1OiYi0gR1OKWwYIvXIzU7zG5ObncbCg4hIQ3jZhVQvb1Y6AKCwxOY1AqLXdRQe7v1ERKQNvOxCmtHS5sKm0rM4V9uI1PgYLMgawhEPIiKVkPL9zZEP0gxDpB6Ls4eGOg0iIuol/tlIREREimLxQURERIpi8UFERESK4pwP0gxOOCUiCg8sPkgTCnZVdFlq+9KuL7jUlohIg1h8kOoV7KrAWwdsXba7BHi2swAhItIOjlmTqrW0uVBY0rXwuFZhiQ0tbS6FMiIiot5i8UGqtqn0rN/7ugAdIyCbSs8qkg8REfUeiw9StXO1jbLGERFR6LH4IFVLjRd3y3WxcUREFHqSio+CggKMHz8ecXFxSExMxD333IPKykqvmMmTJ0On03k9Hn30UVmTpr5jQdYQ6HX+Y/S6jjgiItIGScVHcXExli1bhkOHDmHPnj1obW3FnXfeiYaGBq+43NxcVFVVeR4vv/yyrElT32GI1GPqzYl+Y6benMh+H0REGiJpqW1RUZHX840bNyIxMRFHjx7FpEmTPNtjYmJgsVjkyZD6tHaXgPKvHH5jyr9yoN0lIKKnIRIiIlKFXv25aLfbAQDx8fFe2999910MHDgQGRkZyMvLQ2Oj78mAzc3NcDgcXg8it8O2WlTZnX5jquxOHLbVKpQRERH1VsBNxlwuF1asWIHvfve7yMjI8Gx/4IEHkJqaiqSkJJw4cQJPP/00KisrsW3btm7fp6CgAKtXrw40DQpzNXX+Cw+pcUREFHoBFx/Lli1DeXk5Dh486LV9yZIlnv8eOXIkrFYrpk6dijNnzmDYsGFd3icvLw+rVq3yPHc4HEhOTg40LQoziXFGWeOIiCj0Aio+li9fjg8//BAHDhzA4MGD/cZOmDABAHD69Olui4+oqChERUUFkgb1AZlp8bCajai2O9FdrzEdAIvZiMy0+G72EhGRGkma8yEIApYvX47t27dj3759SEtL6/E1ZWVlAACr1RpQgtS3Reh1yM/xf9+W/Jx0TjYlItIQSSMfy5Ytw5YtW/DBBx8gLi4O1dXVAACz2Yzo6GicOXMGW7ZswaxZs5CQkIATJ05g5cqVmDRpEkaNGhWUA6DwNzPDipSEaJy70tRlX0pCNGZmsLAlItISnSAIPdw545pgXfd/XW7YsAEPPfQQLly4gAcffBDl5eVoaGhAcnIyvv/97+PnP/85TCaTqH/D4XDAbDbDbreLfg2Ftzmvl+DERd+roEYNNmHH8mwFMyIios6kfH9LGvnoqU5JTk5GcXGxlLck8qve2ea38ACAExcdqHe2IdYY8PxpIiJSENtCkqqtfO+4rHFERBR6LD5I1c5/03WeR2/iiIgo9Fh8kKqlXBctaxwREYUeiw9StXXzxsgaR0REocfig1Qt1hiJUYP9z5oeNdjEyaZERBrC4oNUb8fybJ8FCJfZEhFpD/9cJE3YsTwb9c42rHzvOM5/04SU66Kxbt4YjngQEWkQf3OTZsQaI1G4aHyo0yAiol7iZRciIiJSFIsPIiIiUhSLDyIiIlIUiw8iIiJSFIsPIiIiUhRXu5BmtLS5sKn0LM7VNiI1PgYLsobAEMn6mYhIa1h8kCYU7KpAYYkNLuE/217a9QVys9OQNys9dIkREZFkLD5I9Qp2VeCtA7Yu210CPNtZgBARaQfHrEnVWtpcKCzpWnhcq7DEhpY2l0IZERFRb7H4IFXbVHrW61JLd1xCRxwREWkDiw9StXO1jbLGERFR6LH4IFVLjY+RNY6IiEKPxQep2oKsIdDr/MfodR1xRESkDSw+SNUMkXrkZqf5jcnNTmO/DyIiDeFSW1I99zLazn0+9DqwzwcRkQbpBEHoYS2BshwOB8xmM+x2O0wmU6jTIRVhh1MiIvWS8v3NkQ/SDEOkHouzh4Y6DSIi6iUWH0SkiHpnG1a+dxznv2lCynXRWDdvDGKN/BWkJTyH2tfuEnDYVouaOicS44zITItHRE+z+oOAl12IKOjmvF6CExcdXbaPGmzCjuXZIciIpOI51L6i8iqs3lmBKrvTs81qNiI/Jx0zM6y9fn8p39+8YE5EQeXrSwsATlx0YM7rJQpnRFLxHGpfUXkVlm4+5lV4AEC13Ymlm4+hqLxK0XxYfBBR0NQ723x+abmduOhAvbNNoYxIKp5D7Wt3CVi9swLdXeZwb1u9swLtPd3LQkYsPkgzmlra8ez7J7Hg7c/x7Psn0dTSHuqUqAcr3zsuaxwpj+dQ+w7baruMeFxLAFBld+KwrVaxnCQVHwUFBRg/fjzi4uKQmJiIe+65B5WVlV4xTqcTy5YtQ0JCAmJjYzF37lxcvnxZ1qSp78l95whufq4Imw6dR8mpf2HTofO4+bki5L5zJNSpkR/nv2mSNY6Ux3OofTV1vguPQOLkIKn4KC4uxrJly3Do0CHs2bMHra2tuPPOO9HQ0OCJWblyJXbu3Ik///nPKC4uxqVLl3DvvffKnjj1HbnvHMGeippu9+2pqGEBomIp10XLGkfK4znUvsQ4o6xxcujVapevv/4aiYmJKC4uxqRJk2C323H99ddjy5Yt+H//7/8BAP7xj3/g5ptvRmlpKW699dYe35OrXehaTS3tuPm5oh7jvvjFTEQbIhTIiKSod7Yh4/ndPcaVPz+DSzZViudQ+9pdAiau3Ydqu7PbeR86ABazEQefntKrZbeKrXax2+0AgPj4eADA0aNH0draimnTpnliRowYgZSUFJSWlnb7Hs3NzXA4HF4PIrdf7qqQNY6UFWuMxKjB/n8JjRps4peWivEcal+EXof8nI7bUHQuLdzP83PSFe33EXDx4XK5sGLFCnz3u99FRkYGAKC6uhoGgwEDBgzwih00aBCqq6u7fZ+CggKYzWbPIzk5OdCUKAydvdIoaxwpb8fybJ9fXuwRoQ08h9o3M8OK9Q+OhcXsfWnFYjZi/YNjZenzIUXApeqyZctQXl6OgwcP9iqBvLw8rFq1yvPc4XCwACGPIQkxKDklLo7Ua8fybHbH1DieQ+2bmWHF9HSLKjqcBvRTs3z5cnz44Yc4cOAABg8e7NlusVjQ0tKCq1eveo1+XL58GRaLpdv3ioqKQlRUVCBpUB/w01np2HTovKg4UrdYYyQKF40PdRrUCzyH2heh1yFrWEKo05B22UUQBCxfvhzbt2/Hvn37kJaW5rV/3Lhx6NevH/bu3evZVllZifPnzyMrK0uejKlPiTZEYHp6ot+Y6emJnGxKRKQhkkY+li1bhi1btuCDDz5AXFycZx6H2WxGdHQ0zGYzFi9ejFWrViE+Ph4mkwmPPfYYsrKyRK10IepO4cLxPpfbTk9PROFC/iWmBfbGVjy88TAu2Z1IMhvx+4cyYY7pF+q0SAK13JSMtE9S8bF+/XoAwOTJk722b9iwAQ899BAAYN26ddDr9Zg7dy6am5sxY8YMvPnmm7IkS33XPy/XSdpO6nL7K/tw7sp/mlBV2Z0Y/Yv/RWpCNIqfnBLCzEisYN+UjPoW3tWWVK/zF1dn/AJTN54/7XPflKzzl4V7zCMUqyVIfXhXWwob9sZWv19cAHDuShPsja0KZURS8PxpnxpvSkbax+KDVO3hjYdljSNl8fxpnxpvSkbax+KDVO2Sn196gcSRsnj+tE+NNyUj7eszxUe7S0DpmSv4oOwrlJ65wiFCjUgyi7vRkdg4UhbPn/ap8aZkpH19ojUdZ2lr1+8fysToX/yvqDhSH54/7ctMi4fVbOzxpmSZafFKp0YaFvYjH+5Z2p2vWVbbnVi6+RiKyqtClBmJYY7p1+VGSJ3p/h1H6mOO6YfUBP+3Wk9NiOb5UzE13pSMtC+siw/O0ta+rx3N3Z6/awn/jiN1Kn5yis8ChMtstUFtNyUj7QvrPh+lZ67g/sJDPcb9MfdWVfS6p64mrtmLi1d7nsg2eIARB5+ZqkBGFCh2ONU+djglf6R8f4f1nA/O0ta+2gZx/R/ExlHomGP64S8//m6o06BeUMtNyUj7wvqyC2dpa198f3F/GYuNIyKi0Avr4sM9S9vXoKAOHateOEtbvbb/eKKscUREFHphXXxcO0vbF87SVrfrTVEwGf1fHTQZI3G9KUqhjIiIqLfCuvgAOmZpL5mUhs71hV4HLJmUxlnaGnDi+RmI9PGTGqnv2E9ERNoR9sVHUXkVfnfAhs6raQUB+N0BG/t8aEDuO0fQ5up+X5urYz+pX1NLO559/yQWvP05nn3/JJpa2kOdEknU0ubC2yVf4rkPyvF2yZdo8fXBJOpBWC+1bXcJmLh2n8+bIrk78x18egovvahUU0s7bn6uqMe4L34xE9GGCAUyokDkvnMEeypqumyfnp6IwoXjQ5ARSVWwqwKFJd5/yOl1QG52GvJm+b+8TX2DlO/vsB754N0Yte+XuypkjSPl+So8AGBPRQ1HrjSgYFcF3upmBNklAG8dsKGAnz+SKKyLD/b50L6zVxpljSNlNbW0+yw83PZU1PASjIq1tLlQWGLzG1NYYuMlGJIkrIsP9vnQviEJMbLGkbI4cqV9m0rPdhnx6MwldMQRiRXWxQf7fGjfT0VeSxYbR8riyJX2nasVd27ExhEBYV588G6M2hdtiMD09ES/MdPTEznZVKU4cqV9qfHizo3YOCIgzIsPgHdjDAfFlV/3aj+FDkeutG9B1pAufZI60+s64ojECusby7nNzLBierqFd2PUoK9qm9DS7v+Cc0u7gK9qm3BDfPe3bafQsTeKu+GfvbGVo1cqZYjUIzc7DW8d8D3pNDc7DQZfnQCJutFnflrcd2O8+9s3IGtYAgsPjbjr1WJZ40hZ33vtgKxxFBp5s9LxiI9O0Y9MYp8Pkq5PjHyQdjU0i1uCKTaOlGV3tskaR6GTNysdT9w5AptKz+JcbSNS42OwIGsIRzwoICw+SNX6R0XA4ey5sOgfxSF7NRL7tcSvL20wROqxOHtoqNOgMMDPPKnaBz/OljWOlPWt68QVhWLjiCg8cOSDVG1f5WXRcYsT+ReZ2lTWirucIjaOQut0dT3uerUYrS6gnx74+L9ux42W2FCnRRLUO9uw8r3jOP9NE1Kui8a6eWMQa1S+FGDxQarGJlXa1lNnTKlxFDppz3yEa09TqwuY9uti6ADY1swOVVokwZzXS3DiosPzvLK6DhnP78aowSbsWK7s6DEvu5DKif1W4reXGomdi8M5O+rWufC4lvDv/aRunQuPa5246MCc10sUzYfFB6laujVO1jhS1q/mjJY1jpR3urq+x9Je+HccqVO9s81n4eF24qID9QquOpNcfBw4cAA5OTlISkqCTqfD+++/77X/oYcegk6n83rMnDlTrnypj9n3D3HdS8XGkbJ+9KdjssaR8thrR/tWvndc1jg5SC4+GhoaMHr0aLzxxhs+Y2bOnImqqirP449//GOvkqS+q6lV3G26xcYRkTRiP1r8CKrX+W+aZI2Tg+QJp3fddRfuuusuvzFRUVGwWCwBJ0XkljYwBgdPi4sjIvn104srLPrxIr5qpVwXjcrqOlFxSgnKj8v+/fuRmJiI4cOHY+nSpbhy5Uow/hnqA3hjMm37y5LbZI0j5X38X7fLGkfKWzdvjKxxcpC9+Jg5cybeeecd7N27F2vXrkVxcTHuuusutLd336WyubkZDofD60HkFm2IwPT0RL8x09MTeVMylWoRxI3Fi40j5d1oiUVPd8LS/TuO1CnWGIlRg01+Y0YNNina70P24uO+++7DnDlzMHLkSNxzzz348MMPceTIEezfv7/b+IKCApjNZs8jOTlZ7pRI4y47nL3aT6FTUyfu3IiNo9CwrZntswBhnw9t2LE822cBEoo+H0Evc4YOHYqBAwfi9OnTmDp1apf9eXl5WLVqlee5w+FgAUIeUpaIhaJLH/mXGGeUNY5Cx7ZmNjucatyO5dmq6XAa9ClCFy9exJUrV2C1WrvdHxUVBZPJ5PUgclPjEjESLzMtHlaz0e9fzVazEZlp8UqmRQGKNUbCHN0PhggdzNH9WPBrUIReB4vZiMS4KFjMRkToe7qoFhySf3Lq6+tx+vR/lh/YbDaUlZUhPj4e8fHxWL16NebOnQuLxYIzZ87gqaeewo033ogZM2bImjj1DWpcIkbiReh1yM9Jx6Obu+/jIQDIz0kP2S9AEu/mZz/2WtL+r4ZW3LpmL6L76fHFC/5XQJI65L5zBHsqajzPS04Bmw6dx/T0RBQuHK9oLpJHPv72t79hzJgxGDOmY1bsqlWrMGbMGDz33HOIiIjAiRMnMGfOHNx0001YvHgxxo0bh5KSEkRFRcmePIU/sUu/lFwiRtIs3+K/gVhP+yn0Ohce12pqdeHmZz9WOCOSqnPhca09FTXIfeeIovlIHvmYPHkyBMF3s93du3f3KiGia62dOxp7XtwjKo7U5/y/GtHWw0KWNldHXAp7tahS9VVnj038mlpdqL7qhGUA5+6oUVNLu8/Cw21PRQ2aWtoVWznItjCkatuPX5Q1jpQ18zfiWm6LjSPlfe+1A7LGkfJ+uatC1jg5sPggVTtX2yhrHCmL7fG1zyHyZmNi40h5Z6+I+/0oNk4OLD5I1VLjxQ3Fi40jZUWL7LktNo6UZxK5okVsHClvSIK4349i4+TATzyp2rzxKbLGkbL+55HvyhpHyvvwsUmyxpHy1HibChYfpGrvHTkvaxwpa93eSlnjSHmWAb77tLjp/h1H6qTG21Sw+CBV45wPbWOfFu2rd7bB9/rGDsK/40i9CheO91mAhKLPBy/Skapxzoe2qfFW3iSNlC7DhYuU/QIjaQoXjkdTSzt+uasCZ680YkhCDH46Kz0kN+Zk8UGqtiBrCF7a9QVcfv700us64kh91s0bg4zne+79o+StvEkajl6Fl2hDBF64Z2So0+BlF1I3Q6QeudlpfmNys9NgiOSPshqp8VbeJA27DFMw8Dc2qV7erHQ8MikNnW//odcBj0xKQ56CM7RJOrXdypukETsqxdErkoJ/bpAm5M1KxxN3jsCm0rM4V9uI1PgYLMgawhEPjVDTrbxJGvfo1YmLDp8xHL0iqXSCvxu1hIDD4YDZbIbdbofJ5H+4loiIlDHn9ZJuCxCOXpGblO9vlqpERNQjjl6RnPhTQ0REosQaI7mclmTBC+ZERESkKI58EJEi2l0CDttqUVPnRGKcEZlp8YjovISJiPoEFh9EFHRF5VVYvbMCVXanZ5vVbER+TjpmZlhDmBkRhQIvuxBRUBWVV2Hp5mNehQcAVNudWLr5GIrKq0KUGRGFCosPIgqadpeA1Tsrur0xmXvb6p0VaPfXP5+Iwg6LDyIKmsO22i4jHtcSAFTZnThsq1UuKSIKORYfRBQ0NXW+C49A4ogoPLD4IKKgSYwzyhpHROGBq12IKGgy0+JhNRtRbXd2O+9DB8Bi7lh2S+rH5dIkFxYfRBQ0EXod8nPS8ejmY93uFwDk56TzC0wDuFya5MTLLkQUVG/uP92r/RR6XC5NcmPxQURBU+9s83srdgA4cdGBemebQhmRVFwuTcHA4oOIgmble8dljSPlcbk0BQOLDyIKmvPfNMkaR8rjcmkKBhYfRBQ0KddFyxpHyuNyaQoGFh9EFDTr5o2RNY6U514u7Ws9kg4dq164XJqkYPFBREETa4zEqMEmvzGjBpsQa+Sqf7VyL5cG0KUAcT/ncmmSSnLxceDAAeTk5CApKQk6nQ7vv/++135BEPDcc8/BarUiOjoa06ZNw6lTp+TKl4g0ZsfybJ8FyKjBJuxYnq1wRiTVzAwr1j84Fhaz96UVi9mI9Q+OZZ8PkkzynxsNDQ0YPXo0Hn74Ydx7771d9r/88st49dVX8Yc//AFpaWl49tlnMWPGDFRUVMBo5DVBor5ox/Js1DvbsPK94zj/TRNSrovGunljOOKhITMzrJiebmGHU5KFThCEgBdn63Q6bN++Hffccw+AjlGPpKQkPPHEE/jJT34CALDb7Rg0aBA2btyI++67r8f3dDgcMJvNsNvtMJn8D9cSERGROkj5/pZ1zofNZkN1dTWmTZvm2WY2mzFhwgSUlpZ2+5rm5mY4HA6vBxEREYUvWYuP6upqAMCgQYO8tg8aNMizr7OCggKYzWbPIzk5Wc6UiIiISGVCvtolLy8Pdrvd87hw4UKoUyIiIqIgkrX4sFgsAIDLly97bb98+bJnX2dRUVEwmUxeDyIiIgpfshYfaWlpsFgs2Lt3r2ebw+HA559/jqysLDn/KSIiItIoyevc6uvrcfr0f26BbbPZUFZWhvj4eKSkpGDFihV48cUX8a1vfcuz1DYpKcmzIoaIiIj6NsnFx9/+9jfccccdnuerVq0CACxatAgbN27EU089hYaGBixZsgRXr17FxIkTUVRUxB4fREREBKCXfT6CgX0+iIiItCdkfT6IiIiIeqK63sbugRg2GyMiItIO9/e2mAsqqis+6urqAIDNxoiIiDSorq4OZrPZb4zq5ny4XC5cunQJcXFx0OnkvWGRw+FAcnIyLly4EJbzScL9+IDwP0Yen/aF+zHy+LQvWMcoCALq6uqQlJQEvd7/rA7VjXzo9XoMHjw4qP9GuDczC/fjA8L/GHl82hfux8jj075gHGNPIx5unHBKREREimLxQURERIrqU8VHVFQU8vPzERUVFepUgiLcjw8I/2Pk8WlfuB8jj0/71HCMqptwSkREROGtT418EBERUeix+CAiIiJFsfggIiIiRbH4ICIiIkWFTfHR3t6OZ599FmlpaYiOjsawYcPwwgsv9Nhjfv/+/Rg7diyioqJw4403YuPGjcokLFEgx7d//37odLouj+rqagUzF6+urg4rVqxAamoqoqOjcdttt+HIkSN+X6OV8+cm9RjVfg4PHDiAnJwcJCUlQafT4f333/faLwgCnnvuOVitVkRHR2PatGk4depUj+/7xhtvYMiQITAajZgwYQIOHz4cpCPwLxjH9/zzz3c5nyNGjAjiUfjW0/Ft27YNd955JxISEqDT6VBWVibqff/85z9jxIgRMBqNGDlyJHbt2iV/8iIF4xg3btzY5RwajcbgHEAP/B1fa2srnn76aYwcORL9+/dHUlISFi5ciEuXLvX4vsH+DIZN8bF27VqsX78er7/+Or744gusXbsWL7/8Ml577TWfr7HZbJg9ezbuuOMOlJWVYcWKFfjRj36E3bt3K5i5OIEcn1tlZSWqqqo8j8TERAUylu5HP/oR9uzZg02bNuHkyZO48847MW3aNHz11Vfdxmvp/LlJPUY3tZ7DhoYGjB49Gm+88Ua3+19++WW8+uqr+O1vf4vPP/8c/fv3x4wZM+B0On2+53vvvYdVq1YhPz8fx44dw+jRozFjxgzU1NQE6zB8CsbxAcAtt9zidT4PHjwYjPR71NPxNTQ0YOLEiVi7dq3o9/zrX/+K+++/H4sXL8bx48dxzz334J577kF5eblcaUsSjGMEOrqDXnsOz507J0e6kvk7vsbGRhw7dgzPPvssjh07hm3btqGyshJz5szx+56KfAaFMDF79mzh4Ycf9tp27733CvPnz/f5mqeeekq45ZZbvLbNmzdPmDFjRlBy7I1Aju/TTz8VAAjffPNNkLPrvcbGRiEiIkL48MMPvbaPHTtW+NnPftbta7R0/gQhsGPU0jkEIGzfvt3z3OVyCRaLRXjllVc8265evSpERUUJf/zjH32+T2ZmprBs2TLP8/b2diEpKUkoKCgISt5iyXV8+fn5wujRo4OYaWA6H9+1bDabAEA4fvx4j+/zgx/8QJg9e7bXtgkTJgiPPPKIDFn2jlzHuGHDBsFsNsuamxz8HZ/b4cOHBQDCuXPnfMYo8RkMm5GP2267DXv37sU///lPAMD//d//4eDBg7jrrrt8vqa0tBTTpk3z2jZjxgyUlpYGNddABHJ8bt/+9rdhtVoxffp0fPbZZ8FONSBtbW1ob2/vMnQZHR3t869CLZ0/ILBjdNPCOezMZrOhurra6xyZzWZMmDDB5zlqaWnB0aNHvV6j1+sxbdo01Z3XQI7P7dSpU0hKSsLQoUMxf/58nD9/PtjpKkZrn8tA1dfXIzU1FcnJybj77rvx97//PdQpiWK326HT6TBgwIBu9yv1GQyb4uOZZ57BfffdhxEjRqBfv34YM2YMVqxYgfnz5/t8TXV1NQYNGuS1bdCgQXA4HGhqagp2ypIEcnxWqxW//e1v8Ze//AV/+ctfkJycjMmTJ+PYsWMKZi5OXFwcsrKy8MILL+DSpUtob2/H5s2bUVpaiqqqqm5fo6XzBwR2jFo6h52556V0d458zVn517/+hfb2dkmvCZVAjg8AJkyYgI0bN6KoqAjr16+HzWZDdnY26urqgpqvUnx9LtV2/npj+PDh+P3vf48PPvgAmzdvhsvlwm233YaLFy+GOjW/nE4nnn76adx///0+byin1GdQdXe1DdSf/vQnvPvuu9iyZQtuueUWzxyApKQkLFq0KNTp9Vogxzd8+HAMHz7c8/y2227DmTNnsG7dOmzatEmp1EXbtGkTHn74Ydxwww2IiIjA2LFjcf/99+Po0aOhTk02Uo9Ra+eQenbtaOWoUaMwYcIEpKam4k9/+hMWL14cwsxIrKysLGRlZXme33bbbbj55pvx1ltv4YUXXghhZr61trbiBz/4AQRBwPr160OdTviMfDz55JOe0YGRI0diwYIFWLlyJQoKCny+xmKx4PLly17bLl++DJPJhOjo6GCnLEkgx9edzMxMnD59OkhZ9s6wYcNQXFyM+vp6XLhwAYcPH0ZrayuGDh3abbyWzp+b1GPsjprP4bUsFgsAdHuO3Ps6GzhwICIiIiS9JlQCOb7uDBgwADfddJMmzqkYvj6Xajt/cnKPRqv1HLoLj3PnzmHPnj0+Rz0A5T6DYVN8NDY2Qq/3PpyIiAi4XC6fr8nKysLevXu9tu3Zs8erolWLQI6vO2VlZbBarXKmJrv+/fvDarXim2++we7du3H33Xd3G6el89eZ2GPsjhbOIQCkpaXBYrF4nSOHw4HPP//c5zkyGAwYN26c12tcLhf27t2ruvMayPF1p76+HmfOnNHEORVDy5/LQLW3t+PkyZOqPIfuwuPUqVP45JNPkJCQ4Ddesc+gbFNXQ2zRokXCDTfcIHz44YeCzWYTtm3bJgwcOFB46qmnPDHPPPOMsGDBAs/zL7/8UoiJiRGefPJJ4YsvvhDeeOMNISIiQigqKgrFIfgVyPGtW7dOeP/994VTp04JJ0+eFB5//HFBr9cLn3zySSgOoUdFRUXCxx9/LHz55ZfC//7v/wqjR48WJkyYILS0tAiCoO3z5yb1GNV+Duvq6oTjx48Lx48fFwAIv/rVr4Tjx497ZtKvWbNGGDBggPDBBx8IJ06cEO6++24hLS1NaGpq8rzHlClThNdee83zfOvWrUJUVJSwceNGoaKiQliyZIkwYMAAobq6OiyO74knnhD2798v2Gw24bPPPhOmTZsmDBw4UKipqVHd8V25ckU4fvy48NFHHwkAhK1btwrHjx8XqqqqPO+xYMEC4ZlnnvE8/+yzz4TIyEjhv//7v4UvvvhCyM/PF/r16yecPHlS8eMThOAc4+rVq4Xdu3cLZ86cEY4ePSrcd999gtFoFP7+97+r6vhaWlqEOXPmCIMHDxbKysqEqqoqz6O5udnzHqH4DIZN8eFwOITHH39cSElJEYxGozB06FDhZz/7mdf/4EWLFgm333671+s+/fRT4dvf/rZgMBiEoUOHChs2bFA2cZECOb61a9cKw4YNE4xGoxAfHy9MnjxZ2LdvXwiyF+e9994Thg4dKhgMBsFisQjLli0Trl696tmv5fPnJvUY1X4O3UuBOz8WLVokCELHctRnn31WGDRokBAVFSVMnTpVqKys9HqP1NRUIT8/32vba6+9JqSkpAgGg0HIzMwUDh06pNAReQvG8c2bN0+wWq2CwWAQbrjhBmHevHnC6dOnFTyq/+jp+DZs2NDt/muP5/bbb/fEu/3pT38SbrrpJsFgMAi33HKL8NFHHyl3UJ0E4xhXrFjh+fkcNGiQMGvWLOHYsWPKHti/+Ts+9/Lh7h6ffvqp5z1C8RnUCUIPLUCJiIiIZBQ2cz6IiIhIG1h8EBERkaJYfBAREZGiWHwQERGRolh8EBERkaJYfBAREZGiWHwQERGRolh8EBERkaJYfBAREZGiWHwQERGRolh8EBERkaJYfBAREZGi/j+ktWVCcDC6BgAAAABJRU5ErkJggg==",
+      "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)"
@@ -81,13 +422,310 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x28ba26d0310>"
+      ]
+     },
+     "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": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077526\n",
+      "-0.1667332249274541\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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)"
+   ]
+  },
+  {
+   "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')"
+   ]
+  },
+  {
+   "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\\jnopa\\AppData\\Local\\Temp\\ipykernel_7552\\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": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LinearRegression()\n",
+    "model.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 1.79 (11.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = model.predict(X_test)\n",
+    "mse = np.sqrt(mean_squared_error(y_test,prediction))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(prediction)*100:3.3}%)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Determination:  0.054829408658436773\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.score(X_train,y_train)\n",
+    "print('Model Determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x28bbcdf95a0>]"
+      ]
+     },
+     "execution_count": 24,
+     "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)\n",
+    "plt.plot(X_test, prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -106,7 +744,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.10.11"
   },
   "orig_nbformat": 2
  },
diff --git a/2-Regression/4-Logistic/notebook.ipynb b/2-Regression/4-Logistic/notebook.ipynb
index 7c212763..101ea5e6 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": 3,
    "metadata": {},
    "outputs": [
     {
@@ -211,7 +211,7 @@
        "[5 rows x 26 columns]"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,6 +224,104 @@
     "\n",
     "full_pumpkins.head()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.info of       City Name       Package            Variety    Origin Item Size   Color\n",
+       "2     BALTIMORE  24 inch bins        HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3     BALTIMORE  24 inch bins        HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4     BALTIMORE  24 inch bins        HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5     BALTIMORE  24 inch bins        HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6     BALTIMORE  36 inch bins        HOWDEN TYPE  MARYLAND       med  ORANGE\n",
+       "...         ...           ...                ...       ...       ...     ...\n",
+       "1694  ST. LOUIS  24 inch bins  HOWDEN WHITE TYPE  ILLINOIS      xlge   WHITE\n",
+       "1695  ST. LOUIS  24 inch bins  HOWDEN WHITE TYPE  ILLINOIS      xlge   WHITE\n",
+       "1696  ST. LOUIS  24 inch bins  HOWDEN WHITE TYPE  ILLINOIS      xlge   WHITE\n",
+       "1697  ST. LOUIS  24 inch bins  HOWDEN WHITE TYPE  ILLINOIS      xlge   WHITE\n",
+       "1698  ST. LOUIS  24 inch bins  HOWDEN WHITE TYPE  ILLINOIS      xlge   WHITE\n",
+       "\n",
+       "[991 rows x 6 columns]>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns_to_select = ['City Name', 'Package', 'Variety','Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:,columns_to_select]\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "pumpkins.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
+      "  self._figure.tight_layout(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x27b75698880>"
+      ]
+     },
+     "execution_count": 5,
+     "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",
+    "\n",
+    "palette = {\n",
+    "'ORANGE': 'orange',\n",
+    "'WHITE': 'wheat',\n",
+    "}\n",
+    "\n",
+    "sns.catplot(\n",
+    "data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -242,7 +340,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.10.11"
   },
   "metadata": {
    "interpreter": {
diff --git a/3-Web-App/1-Web-App/notebook.ipynb b/3-Web-App/1-Web-App/notebook.ipynb
index 64dad2b3..4507ecf3 100644
--- a/3-Web-App/1-Web-App/notebook.ipynb
+++ b/3-Web-App/1-Web-App/notebook.ipynb
@@ -1,5 +1,407 @@
 {
  "cells": [
+  {
+   "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>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": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('./data/ufos.csv')\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "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>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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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       0.83      0.24      0.37       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                           0.96      5173\n",
+      "   macro avg       0.96      0.85      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:  0.9609510922095496\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: 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\n",
+    "from sklearn.linear_model import LogisticRegression\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('Accuracy: ', accuracy_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\DEV WORK\\Data Science Library\\ML-For-Beginners\\.venv\\lib\\site-packages\\sklearn\\base.py:464: 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",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -14,6 +416,18 @@
    "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": "3.10.11"
+  },
   "orig_nbformat": 4
  },
  "nbformat": 4,
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