logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/usr/local/lib/python3.5/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\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",
      "'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": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 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\n",
    "\n",
    "# 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",
    "\n",
    "# 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)\n",
    "\n",
    "# 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)\n",
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 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(('red', '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(('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()\n",
    "\n",
    "# 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(('red', '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(('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": []
  }
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