Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [2,3]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Dataset into the Training Set and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = sc_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Fitting Logistic Regression to the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = LogisticRegression(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3 - Predicting the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "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, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4 - Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 5 - Visualizing the Training Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_set, y_set = X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Dh4HUCSOvjfjfQFN22tra0BcUXgJGYMrqKZx02titF1f+bCXnzjuX2jtqafpDE6vuHrv1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\nxpgyY4HfGGPKzP8HJEnzjUFZOmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177c6584be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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('Classifier (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_set, y_set = X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MTqle381jq2sbKbv8TeqPtF/x9+41iaqH3H1ySOX56b5WKucVSllW398ql66C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\nPGOB3xhj8sz/D9Icn7bATkryAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177c6584400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('orange', 'pink')))\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', 'yellow'))(i), label = j)\n",
    "plt.title('Classifier (Test Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}