Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Social_Network_Ads.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>...</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>Female</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>Male</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>Female</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>Male</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>Female</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    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",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863  Female   46            41000          1\n",
       "396  15706071    Male   51            23000          1\n",
       "397  15654296  Female   50            20000          1\n",
       "398  15755018    Male   36            33000          0\n",
       "399  15594041  Female   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:,[2,3]].values\n",
    "y=data.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "       [    51,  23000],\n",
       "       [    50,  20000],\n",
       "       [    36,  33000],\n",
       "       [    49,  36000]], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_x=StandardScaler()\n",
    "x_train=sc_x.fit_transform(x_train)\n",
    "x_test=sc_x.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = LogisticRegression(random_state=0)"
   ]
  },
  {
   "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": [
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63  5]\n",
      " [ 8 24]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(63+24)/(63+5+8+24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "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(('yellow', 'orange')))\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(('red', '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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "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', 'black')))\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(('red', '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 3.8.1 32-bit",
   "language": "python",
   "name": "python38132bit5c527499ebc44ab9aebc062fc09d457a"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}