Day 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Social Network Ads Challenges Prediction using Random Forest Classifier Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries\n",
    "import numpy as np                 # used for multi dimensional array\n",
    "import pandas as pd                # used for import the dataset\n",
    "import matplotlib.pyplot as plt        # used for plot the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the dataset\n",
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:,[2, 3]].values    # predictor attribute\n",
    "y = dataset.iloc[:,-1].values        # target attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset into test set and train 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=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test  = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import random forest classifier model from scikit learn\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the model\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the train data into our model\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 3: Making a Prediction and Visulaize the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a prediction\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[53,  5],\n",
       "       [ 1, 21]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm= confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.925"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy result\n",
    "from sklearn.metrics import accuracy_score\n",
    "ac= accuracy_score(y_test, y_pred)\n",
    "ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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('Random Forest Classification (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}