Voice gender recognition .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## voice gender recgonition\n",
    "\n",
    "\n",
    "\n",
    "## Table of Contents\n",
    "- [Part I - Data Cleaning](#data_cleaning)\n",
    "- [Part II - Modeling](#modeling)\n",
    "- [Part III - feature extraction](#feature_extraction)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<a id='data_cleaning'></a>\n",
    "### Part I - Data Cleaning\n",
    "\n",
    "To get started, let's import our libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`1.` Now, read in the `voice.csv` data. Store it in `df`. \n",
    "\n",
    "a. Reading the dataset and taking a look at the top few rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "voice = pd.read_csv('voice.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b. taking all the columns of the csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = voice.iloc[:,:-1].values\n",
    "y = voice.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Checking Outliers in voice\n",
    "\n",
    "- Now we plot box and whisker to check if there is outliers in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5, 4, sharex=True, figsize=(20,20))\n",
    "n = 0\n",
    "z = 0\n",
    "for i in voice.columns[:20]:\n",
    "    sns.boxplot(voice[i],orient='v',ax= axes[z,n],)\n",
    "    axes[z,n].set_title(i)\n",
    "    n+=1\n",
    "    if(n == 4):\n",
    "        n=0\n",
    "        z+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c.function to plot the confusion matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_test,y_pred):\n",
    "    cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "    cm = pd.DataFrame(cm)\n",
    "    cm = cm.rename(columns={0: 'Male',1:'Female'}, index={0: 'Male',1:'Female'})\n",
    "    sns.heatmap(cm,annot=True,cmap='Blues', fmt='g');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d. function to plot the learning curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve (estimator,X,y,cv,scoring ='accuracy',n_jobs=-1,train_size=np.linspace(0.01, 1.0, 50)):\n",
    "    from sklearn.model_selection import learning_curve\n",
    "    train_sizes, train_scores, test_scores = learning_curve(estimator, \n",
    "                                                        X, \n",
    "                                                        y,\n",
    "                                                        # Number of folds in cross-validation\n",
    "                                                        cv=cv,\n",
    "                                                        # Evaluation metric\n",
    "                                                        scoring=scoring)\n",
    "                                                    \n",
    "                                                    \n",
    "    # Create means and standard deviations of training set scores\n",
    "    train_mean = np.mean(train_scores, axis=1)\n",
    "    train_std = np.std(train_scores, axis=1)\n",
    "\n",
    "    # Create means and standard deviations of test set scores\n",
    "    test_mean = np.mean(test_scores, axis=1)\n",
    "    test_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    # Draw lines\n",
    "    plt.plot(train_sizes, train_mean, '--', color=\"#111111\",  label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_mean, color=\"#111111\", label=\"Cross-validation score\")\n",
    "\n",
    "    # Draw bands\n",
    "    plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color=\"#DDDDDD\")\n",
    "    plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color=\"#DDDDDD\")\n",
    "\n",
    "    # Create plot\n",
    "    plt.title(\"Learning Curve\")\n",
    "    plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "e. function to check which hyper parameter will give the highest accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gridSearch(estimator, X_train,y_train,X_test,y_test,*args):\n",
    "    score = {'model_name':[],'accuracy':[],'params': []}\n",
    "    for i in args[0]:\n",
    "        for z in args[1]:\n",
    "            if (type(estimator).__name__ == 'SVC'):\n",
    "                estimator.set_params(kernel=i,gamma='scale',C= z)\n",
    "            elif (type(estimator).__name__ == 'RandomForestClassifier'):\n",
    "                estimator.set_params(n_estimators= i, criterion= z)\n",
    "            else:\n",
    "                estimator.set_params(penalty = i , solver = z)\n",
    "                \n",
    "            estimator.fit(X_train,y_train)\n",
    "            y_pred = estimator.predict(X_test)\n",
    "            score['model_name'].append(type(estimator).__name__)\n",
    "            score['accuracy'].append(metrics.accuracy_score(y_test,y_pred))\n",
    "            score['params'].append((i,z))\n",
    "\n",
    "    return pd.DataFrame(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f. This creates a binary column for each category and returns a sparse matrix or dense array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "oneHotEncoder = OneHotEncoder(sparse=False)\n",
    "y = oneHotEncoder.fit_transform(y)\n",
    "y_final = []\n",
    "for z in y:\n",
    "    y_final.append(z[0])\n",
    "y = y_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "g. Splitting the dataset into the Training set and Test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "h. Standardize features by removing the mean and scaling to unit variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='modeling'></a>\n",
    "### Part II - Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i. Fitting SVM to the Training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "kernel =  ['rbf','linear','sigmoid']\n",
    "C = [1,10,20]\n",
    "scoresDf = gridSearch(SVC(random_state = 0),X_train,y_train,X_test,y_test,kernel,C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.987374</td>\n",
       "      <td>(rbf, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>(rbf, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.984848</td>\n",
       "      <td>(rbf, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(linear, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(linear, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(linear, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>(sigmoid, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.786616</td>\n",
       "      <td>(sigmoid, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.782828</td>\n",
       "      <td>(sigmoid, 10)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_name  accuracy         params\n",
       "2        SVC  0.987374      (rbf, 20)\n",
       "1        SVC  0.986111      (rbf, 10)\n",
       "0        SVC  0.984848       (rbf, 1)\n",
       "3        SVC  0.979798    (linear, 1)\n",
       "4        SVC  0.978535   (linear, 10)\n",
       "5        SVC  0.978535   (linear, 20)\n",
       "6        SVC  0.791667   (sigmoid, 1)\n",
       "8        SVC  0.786616  (sigmoid, 20)\n",
       "7        SVC  0.782828  (sigmoid, 10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresDf.sort_values(by='accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9873737373737373"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC(kernel='rbf',C=20,gamma='scale',random_state=0)\n",
    "classifier.fit(X_train,y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- learning curve of SVC.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_learning_curve(classifier,X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- confusion matrix of SVC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "j.  Fitting LogisticRegression to the Training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mido_\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "solver= ['newton-cg','lbfgs','liblinear', 'sag', 'saga']\n",
    "penalty =['l2']\n",
    "scoresDf2 = gridSearch(LogisticRegression(random_state=0),X_train,y_train,X_test,y_test,penalty,solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(l2, lbfgs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>(l2, newton-cg)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>(l2, liblinear)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>(l2, sag)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>(l2, saga)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model_name  accuracy           params\n",
       "1  LogisticRegression  0.978535      (l2, lbfgs)\n",
       "0  LogisticRegression  0.977273  (l2, newton-cg)\n",
       "2  LogisticRegression  0.977273  (l2, liblinear)\n",
       "3  LogisticRegression  0.977273        (l2, sag)\n",
       "4  LogisticRegression  0.977273       (l2, saga)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresDf2.sort_values(by='accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      "0.9785353535353535\n"
     ]
    }
   ],
   "source": [
    "classifier = LogisticRegression(solver='lbfgs',penalty='l2',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",
    "print('Accuracy Score:')\n",
    "print(metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- learning curve of logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "plot_learning_curve(classifier,X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- confusion matrix of logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k. Fitting Random Forest Classifier to the Training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "n_estimator = [5,10,20,30,40,50]\n",
    "criterion = ['gini','entropy']\n",
    "scoresDf3 = gridSearch(RandomForestClassifier(random_state=0),X_train,y_train,X_test,y_test,n_estimator,criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>(20, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.983586</td>\n",
       "      <td>(30, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.983586</td>\n",
       "      <td>(50, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>(50, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.981061</td>\n",
       "      <td>(10, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.981061</td>\n",
       "      <td>(40, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(20, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(30, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(40, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(10, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.976010</td>\n",
       "      <td>(5, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(5, entropy)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model_name  accuracy         params\n",
       "5   RandomForestClassifier  0.986111  (20, entropy)\n",
       "6   RandomForestClassifier  0.983586     (30, gini)\n",
       "11  RandomForestClassifier  0.983586  (50, entropy)\n",
       "10  RandomForestClassifier  0.982323     (50, gini)\n",
       "2   RandomForestClassifier  0.981061     (10, gini)\n",
       "8   RandomForestClassifier  0.981061     (40, gini)\n",
       "4   RandomForestClassifier  0.979798     (20, gini)\n",
       "7   RandomForestClassifier  0.979798  (30, entropy)\n",
       "9   RandomForestClassifier  0.979798  (40, entropy)\n",
       "3   RandomForestClassifier  0.978535  (10, entropy)\n",
       "0   RandomForestClassifier  0.976010      (5, gini)\n",
       "1   RandomForestClassifier  0.974747   (5, entropy)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresDf3.sort_values(by = 'accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      "0.9861111111111112\n"
     ]
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators=20,criterion='entropy',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",
    "print('Accuracy Score:')\n",
    "print(metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- learning curve of random forest classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "plot_learning_curve(classifier,X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- confusion matrix of random forest classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the Confusion Matrix\n",
    "plot_confusion_matrix(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='feature_extraction'></a>\n",
    "### Part III - Feature extraction\n",
    "\n",
    "a. Getting the top 10 features that have a good correlations with labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:   10.6s finished\n",
      "\n",
      "[2020-12-11 00:17:25] Features: 1/10 -- score: 0.9532828282828283[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    2.5s finished\n",
      "\n",
      "[2020-12-11 00:17:28] Features: 2/10 -- score: 0.9706439393939394[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    2.1s finished\n",
      "\n",
      "[2020-12-11 00:17:30] Features: 3/10 -- score: 0.9775883838383839[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    2.1s finished\n",
      "\n",
      "[2020-12-11 00:17:32] Features: 4/10 -- score: 0.9801136363636364[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    2.2s finished\n",
      "\n",
      "[2020-12-11 00:17:35] Features: 5/10 -- score: 0.9813762626262627[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    2.2s finished\n",
      "\n",
      "[2020-12-11 00:17:37] Features: 6/10 -- score: 0.9829545454545454[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    2.2s finished\n",
      "\n",
      "[2020-12-11 00:17:39] Features: 7/10 -- score: 0.9839015151515151[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    2.1s finished\n",
      "\n",
      "[2020-12-11 00:17:41] Features: 8/10 -- score: 0.9845328282828283[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    2.1s finished\n",
      "\n",
      "[2020-12-11 00:17:44] Features: 9/10 -- score: 0.9839015151515151[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    2.2s finished\n",
      "\n",
      "[2020-12-11 00:17:46] Features: 10/10 -- score: 0.9842171717171717"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{1: {'feature_idx': (12,),\n",
       "  'cv_scores': array([0.95328283]),\n",
       "  'avg_score': 0.9532828282828283,\n",
       "  'feature_names': ('12',)},\n",
       " 2: {'feature_idx': (5, 12),\n",
       "  'cv_scores': array([0.97064394]),\n",
       "  'avg_score': 0.9706439393939394,\n",
       "  'feature_names': ('5', '12')},\n",
       " 3: {'feature_idx': (5, 9, 12),\n",
       "  'cv_scores': array([0.97758838]),\n",
       "  'avg_score': 0.9775883838383839,\n",
       "  'feature_names': ('5', '9', '12')},\n",
       " 4: {'feature_idx': (5, 9, 12, 19),\n",
       "  'cv_scores': array([0.98011364]),\n",
       "  'avg_score': 0.9801136363636364,\n",
       "  'feature_names': ('5', '9', '12', '19')},\n",
       " 5: {'feature_idx': (5, 9, 10, 12, 19),\n",
       "  'cv_scores': array([0.98137626]),\n",
       "  'avg_score': 0.9813762626262627,\n",
       "  'feature_names': ('5', '9', '10', '12', '19')},\n",
       " 6: {'feature_idx': (3, 5, 9, 10, 12, 19),\n",
       "  'cv_scores': array([0.98295455]),\n",
       "  'avg_score': 0.9829545454545454,\n",
       "  'feature_names': ('3', '5', '9', '10', '12', '19')},\n",
       " 7: {'feature_idx': (3, 4, 5, 9, 10, 12, 19),\n",
       "  'cv_scores': array([0.98390152]),\n",
       "  'avg_score': 0.9839015151515151,\n",
       "  'feature_names': ('3', '4', '5', '9', '10', '12', '19')},\n",
       " 8: {'feature_idx': (3, 4, 5, 9, 10, 12, 14, 19),\n",
       "  'cv_scores': array([0.98453283]),\n",
       "  'avg_score': 0.9845328282828283,\n",
       "  'feature_names': ('3', '4', '5', '9', '10', '12', '14', '19')},\n",
       " 9: {'feature_idx': (0, 3, 4, 5, 9, 10, 12, 14, 19),\n",
       "  'cv_scores': array([0.98390152]),\n",
       "  'avg_score': 0.9839015151515151,\n",
       "  'feature_names': ('0', '3', '4', '5', '9', '10', '12', '14', '19')},\n",
       " 10: {'feature_idx': (0, 3, 4, 5, 9, 10, 11, 12, 14, 19),\n",
       "  'cv_scores': array([0.98421717]),\n",
       "  'avg_score': 0.9842171717171717,\n",
       "  'feature_names': ('0', '3', '4', '5', '9', '10', '11', '12', '14', '19')}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "\n",
    "sfs1 = SFS(SVC(kernel='rbf',C=20,gamma='scale',random_state=0), \n",
    "           k_features=10, \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           verbose=2,\n",
    "           scoring='accuracy',\n",
    "           cv=0)\n",
    "\n",
    "sfs1 = sfs1.fit(X, y)\n",
    "sfs1.subsets_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = voice.iloc[:,[3, 4, 5, 9, 10, 12, 14, 19,-1]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b. importing dataset after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = selected_features.iloc[:,:-1].values\n",
    "y = selected_features.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "oneHotEncoder = OneHotEncoder(sparse=False)\n",
    "y = oneHotEncoder.fit_transform(y)\n",
    "y_final = []\n",
    "for z in y:\n",
    "    y_final.append(z[0])\n",
    "y = y_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c.Splitting the dataset into the Training set and Test set after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d.Fitting SVM to the Training set after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "kernel =  ['rbf','linear','sigmoid']\n",
    "C = [1,10,20]\n",
    "scoresDf = gridSearch(SVC(random_state = 0),X_train,y_train,X_test,y_test,kernel,C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>(rbf, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(rbf, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(rbf, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.976010</td>\n",
       "      <td>(linear, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(linear, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(linear, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.862374</td>\n",
       "      <td>(sigmoid, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.839646</td>\n",
       "      <td>(sigmoid, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.824495</td>\n",
       "      <td>(sigmoid, 10)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model_name  accuracy         params\n",
       "0        SVC  0.982323       (rbf, 1)\n",
       "1        SVC  0.979798      (rbf, 10)\n",
       "2        SVC  0.979798      (rbf, 20)\n",
       "3        SVC  0.976010    (linear, 1)\n",
       "4        SVC  0.974747   (linear, 10)\n",
       "5        SVC  0.974747   (linear, 20)\n",
       "8        SVC  0.862374  (sigmoid, 20)\n",
       "6        SVC  0.839646   (sigmoid, 1)\n",
       "7        SVC  0.824495  (sigmoid, 10)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresDf.sort_values(by='accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9823232323232324"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC(kernel='rbf',C=1,gamma='scale',random_state=0)\n",
    "classifier.fit(X_train,y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- learning curve of SVC after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_learning_curve(classifier,X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- confusion matrix of SVC after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "e. Fitting Logistic Regression to the Training set after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "solver= ['newton-cg','lbfgs','liblinear', 'sag', 'saga']\n",
    "penalty =['l2']\n",
    "scoresDf2 = gridSearch(LogisticRegression(random_state=0),X_train,y_train,X_test,y_test,penalty,solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(l2, newton-cg)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(l2, lbfgs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(l2, liblinear)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(l2, sag)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.974747</td>\n",
       "      <td>(l2, saga)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model_name  accuracy           params\n",
       "0  LogisticRegression  0.974747  (l2, newton-cg)\n",
       "1  LogisticRegression  0.974747      (l2, lbfgs)\n",
       "2  LogisticRegression  0.974747  (l2, liblinear)\n",
       "3  LogisticRegression  0.974747        (l2, sag)\n",
       "4  LogisticRegression  0.974747       (l2, saga)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresDf2.sort_values(by='accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      "0.9747474747474747\n"
     ]
    }
   ],
   "source": [
    "classifier = LogisticRegression(solver='lbfgs',penalty='l2',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",
    "print('Accuracy Score:')\n",
    "print(metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- learning curve of Logistic Regression after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "plot_learning_curve(classifier,X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Confusion Matrix of Logistic Regression after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "f. Fitting Random Forest Classifier to the Training set after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "n_estimator = [5,10,20,30,40,50]\n",
    "criterion = ['gini','entropy']\n",
    "scoresDf3 = gridSearch(RandomForestClassifier(random_state=0),X_train,y_train,X_test,y_test,n_estimator,criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>(10, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.982323</td>\n",
       "      <td>(40, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(10, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(20, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(40, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(50, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>(50, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(20, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(30, gini)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>(30, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.976010</td>\n",
       "      <td>(5, entropy)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.973485</td>\n",
       "      <td>(5, gini)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model_name  accuracy         params\n",
       "3   RandomForestClassifier  0.982323  (10, entropy)\n",
       "9   RandomForestClassifier  0.982323  (40, entropy)\n",
       "2   RandomForestClassifier  0.979798     (10, gini)\n",
       "5   RandomForestClassifier  0.979798  (20, entropy)\n",
       "8   RandomForestClassifier  0.979798     (40, gini)\n",
       "10  RandomForestClassifier  0.979798     (50, gini)\n",
       "11  RandomForestClassifier  0.979798  (50, entropy)\n",
       "4   RandomForestClassifier  0.978535     (20, gini)\n",
       "6   RandomForestClassifier  0.978535     (30, gini)\n",
       "7   RandomForestClassifier  0.978535  (30, entropy)\n",
       "1   RandomForestClassifier  0.976010   (5, entropy)\n",
       "0   RandomForestClassifier  0.973485      (5, gini)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresDf3.sort_values(by = 'accuracy',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      "0.9823232323232324\n"
     ]
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',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",
    "print('Accuracy Score:')\n",
    "print(metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- learning curve of Random Forest Classifier after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "plot_learning_curve(classifier,X_train,y_train,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Confusion Matrix of Random Forest Classifier after feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)\n"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}