regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "noble-bloom",
   "metadata": {},
   "source": [
    "## Regression\n",
    "> The main difference between **Regression** and **Classification** algorithms that **Regression** algorithms are used to predict the **continuous** values such as **price**, **salary**, **age**, etc. and **Classification** algorithms are used to predict/Classify the discrete values such as **Male or Female**, **True or False**, **Spam or Not Spam**, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rising-wiring",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "stopped-ministry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '_base',\n",
       " '_california_housing',\n",
       " '_covtype',\n",
       " '_kddcup99',\n",
       " '_lfw',\n",
       " '_olivetti_faces',\n",
       " '_openml',\n",
       " '_rcv1',\n",
       " '_samples_generator',\n",
       " '_species_distributions',\n",
       " '_svmlight_format_fast',\n",
       " '_svmlight_format_io',\n",
       " '_twenty_newsgroups',\n",
       " 'clear_data_home',\n",
       " 'dump_svmlight_file',\n",
       " 'fetch_20newsgroups',\n",
       " 'fetch_20newsgroups_vectorized',\n",
       " 'fetch_california_housing',\n",
       " 'fetch_covtype',\n",
       " 'fetch_kddcup99',\n",
       " 'fetch_lfw_pairs',\n",
       " 'fetch_lfw_people',\n",
       " 'fetch_olivetti_faces',\n",
       " 'fetch_openml',\n",
       " 'fetch_rcv1',\n",
       " 'fetch_species_distributions',\n",
       " 'get_data_home',\n",
       " 'load_boston',\n",
       " 'load_breast_cancer',\n",
       " 'load_diabetes',\n",
       " 'load_digits',\n",
       " 'load_files',\n",
       " 'load_iris',\n",
       " 'load_linnerud',\n",
       " 'load_sample_image',\n",
       " 'load_sample_images',\n",
       " 'load_svmlight_file',\n",
       " 'load_svmlight_files',\n",
       " 'load_wine',\n",
       " 'make_biclusters',\n",
       " 'make_blobs',\n",
       " 'make_checkerboard',\n",
       " 'make_circles',\n",
       " 'make_classification',\n",
       " 'make_friedman1',\n",
       " 'make_friedman2',\n",
       " 'make_friedman3',\n",
       " 'make_gaussian_quantiles',\n",
       " 'make_hastie_10_2',\n",
       " 'make_low_rank_matrix',\n",
       " 'make_moons',\n",
       " 'make_multilabel_classification',\n",
       " 'make_regression',\n",
       " 'make_s_curve',\n",
       " 'make_sparse_coded_signal',\n",
       " 'make_sparse_spd_matrix',\n",
       " 'make_sparse_uncorrelated',\n",
       " 'make_spd_matrix',\n",
       " 'make_swiss_roll']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alpha-domestic",
   "metadata": {},
   "source": [
    "### Simple Regression Example using Support Vector Regression (SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "reasonable-yellow",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "naughty-discharge",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[0,0], [2,2], [4, 4], [9,9], [20, 20]])\n",
    "y = np.array([0, 2, 4, 9, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "opposed-description",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVR()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "centered-fitting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.49701406])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[2,2]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "vulnerable-tooth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14982203967374197"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dense-second",
   "metadata": {},
   "source": [
    "### Simple Regression Example using LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "parallel-large",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "loaded-holder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "coordinate-governor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2. ,  0.5, 10. ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[2, 2], [0, 1], [10, 10]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "experimental-fourth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alpha-waters",
   "metadata": {},
   "source": [
    "> **Conclusion**: On linear data `LinearRegression` is the best algorithim for marking predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "extra-edgar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 2, 4, 9, 20], [0, 2, 4, 9, 20])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_1, x_2 = [], []\n",
    "for i in X:\n",
    "    x_1.append(i[0])\n",
    "    x_2.append(i[1])\n",
    "x_1, x_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "silent-aruba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_1, y)\n",
    "plt.scatter(x_2, y, marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protected-sheffield",
   "metadata": {},
   "source": [
    "### Iris Dataset\n",
    "The different flowers has three different categories and 4 featueres which are:\n",
    "        - sepal length in cm\n",
    "        - sepal width in cm\n",
    "        - petal length in cm\n",
    "        - petal width in cm\n",
    "We want to predict the **petal width** of the flower Iris-Setosa given, **(sepal_length, sepal_width, petal_len)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "logical-modeling",
   "metadata": {},
   "outputs": [],
   "source": [
    "# According to the descpr the first 50 flowers are iris_setosa\n",
    "iris = datasets.load_iris()\n",
    "iris_setosa = iris.data[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "correct-timber",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "palestinian-triple",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
       "1  4.9  3.0  1.4  0.2\n",
       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_setosa_dataFrame = pd.DataFrame(iris_setosa)\n",
    "iris_setosa_dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ultimate-mambo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data = iris_setosa_dataFrame)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foster-sculpture",
   "metadata": {},
   "source": [
    "### Splitting data into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "empty-enforcement",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fleet-distinction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[5.1, 3.5, 1.4], [4.9, 3.0, 1.4]], [0.2, 0.2])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = [], []\n",
    "\n",
    "for feature in iris_setosa:\n",
    "    X.append(list(feature[:3]))\n",
    "    y.append(feature[-1])\n",
    "    \n",
    "X[:2], y[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "permanent-tobacco",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "accomplished-fossil",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45, 3), (45,), (5, 3))"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= .1, random_state=33)\n",
    "X_train.shape, y_train.shape, X_test.shape, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "bizarre-edgar",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "constant-cable",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "welsh-trance",
   "metadata": {},
   "source": [
    "### Predictions using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "surprising-letter",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "personal-showcase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_mod = LinearRegression()\n",
    "l_mod.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "medical-extraction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.52096765, -1.08655837, -0.34163533]), 0.3)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0], y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "minute-landing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.22341918])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_mod.predict([[-0.52096765, -1.08655837, -0.34163533]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "legal-purpose",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.30018067 0.25745678 0.23071922 0.28104273 0.21719275]\n",
      "[0.4 0.2 0.1 0.4 0.3]\n"
     ]
    }
   ],
   "source": [
    "print(l_mod.predict(X_test))\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "through-radical",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1185275147409538"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_mod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "surface-civilian",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "parallel-hormone",
   "metadata": {},
   "source": [
    "### Predictions using Support Vector Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "surgical-austin",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "serial-palestinian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_mod = SVR()\n",
    "svm_mod.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "historic-function",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36868764406520393"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_mod.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "international-mentor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19985105])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_mod.predict([X_train[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "enormous-playback",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "integral-honor",
   "metadata": {},
   "source": [
    "## LogisticRegression\n",
    "> So we use Logistic Regression when our independent variable is categorical, for example male(1) or female(0).\n",
    "\n",
    "#### Types of Logistic Regression:\n",
    "1. **Binary Logistic Regression**\n",
    "    * Only two possible outcomes(Category). Example: The person will buy a car or not.\n",
    "\n",
    "2. **Multinomial Logistic Regression**\n",
    "    * More than two Categories possible without ordering.\n",
    "\n",
    "3. **Ordinal Logistic Regression**\n",
    "    * More than two Categories possible with ordering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "italian-celebrity",
   "metadata": {},
   "source": [
    "## The Social_Network_Ads dataset and LogisticRegression\n",
    "> Let's try to predict if a person will by a car or not using `Social_Network_Ads.csv` file downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "perfect-portsmouth",
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Social_Network_Ads.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complex-timer",
   "metadata": {},
   "source": [
    "> Let's assume that `1` means buying a car and `0` means not buying. In this case our dependent variable is `Purchased` and our independent variables are `gender, age, estimated salary`\n",
    "\n",
    "> So we may want to convert gender quantitative data, which means male can be represended by 1 and 0 otherwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "sophisticated-ottawa",
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</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>395</th>\n",
       "      <td>15691863</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0    15624510       1   19            19000          0\n",
       "1    15810944       1   35            20000          0\n",
       "2    15668575       0   26            43000          0\n",
       "3    15603246       0   27            57000          0\n",
       "4    15804002       1   19            76000          0\n",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863       0   46            41000          1\n",
       "396  15706071       1   51            23000          1\n",
       "397  15654296       0   50            20000          1\n",
       "398  15755018       1   36            33000          0\n",
       "399  15594041       0   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data[\"Gender\"] = data['Gender'].replace('Male', 1)\n",
    "data[\"Gender\"] = data['Gender'].replace('Female', 0)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "instrumental-software",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 1:4].values\n",
    "y = data.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "difficult-sudan",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_balanced = data.iloc[:, 1:4].values\n",
    "y_balanced = data.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chubby-purple",
   "metadata": {},
   "source": [
    "## We buying sample to be equal to not buying sample\n",
    "> This data `allPeople` will be used after evaluating the model at the end of testing the model without balanced cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "configured-radius",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204, 196)"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f =0;\n",
    "m = 0\n",
    "for x in X_balanced:\n",
    "    if x[0] == 1:\n",
    "        m +=1\n",
    "    else:\n",
    "        f +=1\n",
    "f, m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "interracial-arrest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 257)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_count = list(y_balanced).count(1)\n",
    "cars_count, list(y_balanced).count(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floppy-milwaukee",
   "metadata": {},
   "source": [
    "> 143 people with cars and 257 people without cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "chronic-orientation",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_balanced = []\n",
    "for x, y in zip(X_balanced, y_balanced):\n",
    "    data_balanced.append([x, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "quick-headset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 143)"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars =[]\n",
    "nocars = []\n",
    "for data__ in data_balanced:\n",
    "    x, y = data__\n",
    "    if y == 1:\n",
    "        cars.append(data__)\n",
    "    else:\n",
    "        nocars.append(data__)\n",
    "    \n",
    "nocars = nocars[:cars_count]\n",
    "cars = cars[:cars_count]\n",
    "\n",
    "len(nocars), len(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "transsexual-museum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[array([    1,    19, 19000], dtype=int64), 0],\n",
       " [array([    1,    35, 20000], dtype=int64), 0],\n",
       " [array([    0,    26, 43000], dtype=int64), 0]]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nocars[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-hamilton",
   "metadata": {},
   "source": [
    "> Number of people with cars are now equal to number of people without cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "uniform-passenger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "286"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allPeople = nocars + cars\n",
    "len(allPeople)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "incredible-feedback",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import shuffle\n",
    "shuffle(allPeople)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "infrared-accessory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 400)"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(allPeople), len(y_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "conditional-physiology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[array([    0,    28, 44000], dtype=int64), 0],\n",
       " [array([    0,    52, 38000], dtype=int64), 1],\n",
       " [array([    1,    19, 76000], dtype=int64), 0],\n",
       " [array([    1,    35, 50000], dtype=int64), 0],\n",
       " [array([     0,     60, 108000], dtype=int64), 1]]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allPeople[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-designer",
   "metadata": {},
   "source": [
    "### Getting X and y Data for balanced sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "independent-hazard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 286, 0, array([    0,    28, 44000], dtype=int64))"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_balanced_data = []\n",
    "y_balanced_data = []\n",
    "for i, j in allPeople:\n",
    "    X_balanced_data.append(i)\n",
    "    y_balanced_data.append(j)\n",
    "\n",
    "len(X_balanced_data), len(X_balanced_data), y_balanced_data[0], X_balanced_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "regulation-animal",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "collective-appendix",
   "metadata": {},
   "source": [
    "### Predicting with unbalances data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "demographic-abraham",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 400)"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X), len(y_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "weird-crime",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "nasty-binary",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y_balanced, random_state=29, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "chief-progress",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sclaled = scaler.fit_transform(X_train)\n",
    "X_test_sclaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "casual-table",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "medium-glance",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "wicked-jonathan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_sclaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "public-enterprise",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data_test = scaler.transform([[46, 41000,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "under-appreciation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "amended-wrong",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -------> 0\n",
      "0 -------> 0\n",
      "0 -------> 1\n",
      "0 -------> 0\n",
      "0 -------> 1\n",
      "0 -------> 0\n",
      "1 -------> 1\n",
      "0 -------> 1\n",
      "1 -------> 1\n",
      "0 -------> 0\n"
     ]
    }
   ],
   "source": [
    "for _data, _y in  list(zip(X_test_sclaled, y_test))[:10]:\n",
    "    print(model.predict([_data])[0],\"------->\",  _y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tough-serial",
   "metadata": {},
   "source": [
    "### Perfomance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "packed-torture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.853125"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train_sclaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increasing-given",
   "metadata": {},
   "source": [
    "> The **model** is `85%` accurate on the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "unknown-filename",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7875"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test_sclaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metallic-baptist",
   "metadata": {},
   "source": [
    "> The model is `79%` accurate on the tests data. Which is very reasonable!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "precious-vegetarian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.853125"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_train_pred = model.predict(X_train_sclaled)\n",
    "acc = accuracy_score(y_train, y_train_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "municipal-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7875"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_test_pred = model.predict(X_test_sclaled)\n",
    "acc = accuracy_score(y_test, y_test_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "vocational-mason",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85714286, 0.58536585])"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test, model.predict(X_test_sclaled), \n",
    "         average=None, labels=[0,  1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operational-terminology",
   "metadata": {},
   "source": [
    "### Predicting After Data has been balanced\n",
    "\n",
    "> The model is more biased It's predicting the data more accurate on not buying a car:\n",
    "\n",
    "> **One way to solve this is to use equal number of females and males in the train and test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "fresh-complement",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "north-washer",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_balanced, X_test_balanced, y_train_balanced, y_test_balanced = train_test_split(X_balanced_data, y_balanced_data, random_state=29, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "beautiful-hamburg",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sclaled_balanced = scaler.fit_transform(X_train_balanced)\n",
    "X_test_sclaled_balanced = scaler.transform(X_test_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "distant-rotation",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "designing-stress",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "consolidated-retro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_sclaled_balanced, y_train_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "sunset-capitol",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data_test_balanced = scaler.transform([[46, 41000,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "interior-transfer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_data_test_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "liquid-attachment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -------> 0\n",
      "0 -------> 0\n",
      "1 -------> 0\n",
      "0 -------> 0\n",
      "0 -------> 0\n",
      "0 -------> 0\n",
      "0 -------> 0\n",
      "0 -------> 0\n",
      "1 -------> 1\n",
      "1 -------> 1\n"
     ]
    }
   ],
   "source": [
    "for _data, _y in  list(zip(X_test_sclaled_balanced, y_test_balanced))[:10]:\n",
    "    print(model.predict([_data])[0],\"------->\",  _y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tamil-soccer",
   "metadata": {},
   "source": [
    "### Perfomance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "designing-sigma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9298245614035088"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train_sclaled_balanced, y_train_balanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "owned-miniature",
   "metadata": {},
   "source": [
    "> The **model** is now `93%` accurate on the train set. We can further imporove the model by balancing the gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "informal-edwards",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.896551724137931"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test_sclaled_balanced, y_test_balanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exceptional-party",
   "metadata": {},
   "source": [
    "> The **model** is now `90%` accurate on the test set. We can improve the model by balancing the gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "valued-mother",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9298245614035088"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_train_pred = model.predict(X_train_sclaled_balanced)\n",
    "acc = accuracy_score(y_train_balanced, y_train_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "expired-tender",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.896551724137931"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_test_pred = model.predict(X_test_sclaled_balanced)\n",
    "acc = accuracy_score(y_test_balanced, y_test_pred)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "quiet-distance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9       , 0.89285714])"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test_balanced, model.predict(X_test_sclaled_balanced), \n",
    "         average=None, labels=[0,  1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alpha-mailman",
   "metadata": {},
   "source": [
    "> A lit bit better now"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alert-baghdad",
   "metadata": {},
   "source": [
    "> Done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "previous-destiny",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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