Logistic-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem: Identify whether a user is going to buy a product based on his/her age and salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[65,  3],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classification template\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 classifier 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 a Confusion Matrix \n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test, Y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the Training Set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": {
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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(('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 Test Set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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(('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 (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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}