Predicting sale using logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15728773</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>58000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15598044</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>84000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15694829</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15600575</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15727311</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>65000</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\n",
       "5  15728773    Male   27            58000          0\n",
       "6  15598044  Female   27            84000          0\n",
       "7  15694829  Female   32           150000          1\n",
       "8  15600575    Male   25            33000          0\n",
       "9  15727311  Female   35            65000          0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Logistic Regression to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('blue', 'green'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('blue', 'green'))(i), label = j)\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}