gaurav_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "#importing dataset\n",
    "datset=pd.read_csv('Social_Network_Ads.csv')\n",
    "X=datset.iloc[:,2:4].values\n",
    "y=datset.iloc[:,-1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting into test and train\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=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lab102-20\\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\\lab102-20\\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\\lab102-20\\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\\lab102-20\\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"
     ]
    }
   ],
   "source": [
    "#feature labeling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_x=StandardScaler()\n",
    "X_train=sc_x.fit_transform(X_train)\n",
    "X_test=sc_x.fit_transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lab102-20\\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"
     ]
    }
   ],
   "source": [
    "#by logistic regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model1=LogisticRegression()\n",
    "model1.fit(X_train,y_train)\n",
    "\n",
    "#prediction the value set\n",
    "y1_pred=model1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model of naive bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "model=GaussianNB()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "#prediction of model value\n",
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of logistic model 88.75\n",
      "accuracy of naive bayes 93.75\n"
     ]
    }
   ],
   "source": [
    "#evaluation of result\n",
    "from sklearn.metrics import confusion_matrix , accuracy_score\n",
    "cm_naive=confusion_matrix(y_test, y_pred)\n",
    "accuracy_naive=accuracy_score(y_test,y_pred)\n",
    "\n",
    "cm_logistic=confusion_matrix(y_test,y1_pred)\n",
    "accuray_logistic=accuracy_score(y_test,y1_pred)\n",
    "\n",
    "print('accuracy of logistic model',accuray_logistic*100)\n",
    "print('accuracy of naive bayes', accuracy_naive*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "#plotting test result of the naive bayes classifier\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, model.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('naive bayes Classifier (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "#plotting test result of logistic classifcation\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, model1.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 bayes 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
}