FacebookCommentPrediction & Admission Prediction.ipynb

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      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: 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"
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       "   PagePopularitylikes   PageCheckin  Pagetalking about   PageCategory   \\\n",
       "0                634995            0                 463              1   \n",
       "1                634995            0                 463              1   \n",
       "2                634995            0                 463              1   \n",
       "3                634995            0                 463              1   \n",
       "4                634995            0                 463              1   \n",
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       "   MinCC1  MaxCC1  AverageCC1  MedianCC1      SDCC1  MinCC2       ...        \\\n",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import os\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "%matplotlib inline\n",
    "os.chdir(\"C:\\Vishakha\\Machine Learning\\Assignment_2\")\n",
    "Dataset1 = pd.read_csv(\"Fb_Data.csv\")\n",
    "Dataset1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facebook Comment Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PagePopularitylikes</th>\n",
       "      <th>PageCheckin</th>\n",
       "      <th>Pagetalking about</th>\n",
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       "      <th>MedianCC1</th>\n",
       "      <th>SDCC1</th>\n",
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       "      <th>count</th>\n",
       "      <td>4.194900e+04</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.373487e+06</td>\n",
       "      <td>4.692329e+03</td>\n",
       "      <td>5.016193e+04</td>\n",
       "      <td>24.198098</td>\n",
       "      <td>1.761329</td>\n",
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       "      <td>36.156678</td>\n",
       "      <td>68.822745</td>\n",
       "      <td>0.319722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148609</td>\n",
       "      <td>0.139097</td>\n",
       "      <td>0.146249</td>\n",
       "      <td>0.134020</td>\n",
       "      <td>0.134354</td>\n",
       "      <td>0.145057</td>\n",
       "      <td>0.149205</td>\n",
       "      <td>0.144819</td>\n",
       "      <td>0.146297</td>\n",
       "      <td>7.782069</td>\n",
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       "      <th>std</th>\n",
       "      <td>6.853878e+06</td>\n",
       "      <td>2.130941e+04</td>\n",
       "      <td>1.380645e+05</td>\n",
       "      <td>19.970488</td>\n",
       "      <td>23.429401</td>\n",
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       "      <td>72.093357</td>\n",
       "      <td>84.366383</td>\n",
       "      <td>13.107268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355707</td>\n",
       "      <td>0.346052</td>\n",
       "      <td>0.353360</td>\n",
       "      <td>0.340678</td>\n",
       "      <td>0.341036</td>\n",
       "      <td>0.352163</td>\n",
       "      <td>0.356295</td>\n",
       "      <td>0.351922</td>\n",
       "      <td>0.353408</td>\n",
       "      <td>38.727624</td>\n",
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       "      <th>min</th>\n",
       "      <td>3.600000e+01</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>4.036800e+04</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>2.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>3.099140e+05</td>\n",
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       "      <td>8.153000e+03</td>\n",
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       "      <td>12.000000</td>\n",
       "      <td>35.853805</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>1.294733e+06</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>5.320700e+04</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>717.000000</td>\n",
       "      <td>72.980769</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>106.366011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.869723e+08</td>\n",
       "      <td>1.100558e+06</td>\n",
       "      <td>6.784263e+06</td>\n",
       "      <td>106.000000</td>\n",
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       "      <td>2858.000000</td>\n",
       "      <td>2341.000000</td>\n",
       "      <td>2341.000000</td>\n",
       "      <td>1101.000000</td>\n",
       "      <td>1923.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2136.000000</td>\n",
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       "  </tbody>\n",
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       "<p>8 rows × 54 columns</p>\n",
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      "text/plain": [
       "       PagePopularitylikes    PageCheckin  Pagetalking about   PageCategory   \\\n",
       "count          4.194900e+04  4.194900e+04        4.194900e+04   41949.000000   \n",
       "mean           1.373487e+06  4.692329e+03        5.016193e+04      24.198098   \n",
       "std            6.853878e+06  2.130941e+04        1.380645e+05      19.970488   \n",
       "min            3.600000e+01  0.000000e+00        0.000000e+00       1.000000   \n",
       "25%            4.036800e+04  0.000000e+00        7.450000e+02       9.000000   \n",
       "50%            3.099140e+05  0.000000e+00        8.153000e+03      18.000000   \n",
       "75%            1.294733e+06  9.900000e+01        5.320700e+04      32.000000   \n",
       "max            4.869723e+08  1.100558e+06        6.784263e+06     106.000000   \n",
       "\n",
       "             MinCC1        MaxCC1    AverageCC1     MedianCC1         SDCC1  \\\n",
       "count  41949.000000  41949.000000  41949.000000  41949.000000  41949.000000   \n",
       "mean       1.761329    449.385921     56.703058     36.156678     68.822745   \n",
       "std       23.429401    507.979665     89.651968     72.093357     84.366383   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000     45.000000      5.703704      2.000000      8.365189   \n",
       "50%        0.000000    241.000000     23.842105     12.000000     35.853805   \n",
       "75%        0.000000    717.000000     72.980769     42.000000    106.366011   \n",
       "max     2341.000000   2858.000000   2341.000000   2341.000000   1101.000000   \n",
       "\n",
       "             MinCC2       ...        PostpublishedFriday  \\\n",
       "count  41949.000000       ...               41949.000000   \n",
       "mean       0.319722       ...                   0.148609   \n",
       "std       13.107268       ...                   0.355707   \n",
       "min        0.000000       ...                   0.000000   \n",
       "25%        0.000000       ...                   0.000000   \n",
       "50%        0.000000       ...                   0.000000   \n",
       "75%        0.000000       ...                   0.000000   \n",
       "max     1923.000000       ...                   1.000000   \n",
       "\n",
       "       PostpublishedSaturday  BaseDateTimeSunday   BaseDateTimeMonday  \\\n",
       "count           41949.000000         41949.000000        41949.000000   \n",
       "mean                0.139097             0.146249            0.134020   \n",
       "std                 0.346052             0.353360            0.340678   \n",
       "min                 0.000000             0.000000            0.000000   \n",
       "25%                 0.000000             0.000000            0.000000   \n",
       "50%                 0.000000             0.000000            0.000000   \n",
       "75%                 0.000000             0.000000            0.000000   \n",
       "max                 1.000000             1.000000            1.000000   \n",
       "\n",
       "       BaseDateTTuesday   BaseDateTimeWednesday  BaseDateTimeThursday   \\\n",
       "count       41949.000000           41949.000000           41949.000000   \n",
       "mean            0.134354               0.145057               0.149205   \n",
       "std             0.341036               0.352163               0.356295   \n",
       "min             0.000000               0.000000               0.000000   \n",
       "25%             0.000000               0.000000               0.000000   \n",
       "50%             0.000000               0.000000               0.000000   \n",
       "75%             0.000000               0.000000               0.000000   \n",
       "max             1.000000               1.000000               1.000000   \n",
       "\n",
       "       BaseDateTimeFriday   BaseDateTimeSaturday   TargetVariable  \n",
       "count         41949.000000           41949.000000    41949.000000  \n",
       "mean              0.144819               0.146297        7.782069  \n",
       "std               0.351922               0.353408       38.727624  \n",
       "min               0.000000               0.000000        0.000000  \n",
       "25%               0.000000               0.000000        0.000000  \n",
       "50%               0.000000               0.000000        0.000000  \n",
       "75%               0.000000               0.000000        3.000000  \n",
       "max               1.000000               1.000000     2136.000000  \n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b80adf87b8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dataset1.columns\n",
    "Dataset1 = pd.DataFrame(Dataset1)\n",
    "Dataset1.columns = Dataset1.columns.to_series().apply(lambda x: x.strip())\n",
    "sns.distplot(Dataset1['PageCategory'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dataset1['New_Target'] = [1 if each > 0 else 0 for each in Dataset1['TargetVariable']]\n",
    "y = Dataset1['New_Target'].values\n",
    "x = Dataset1.drop(['New_Target'],axis = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM for Faceboook Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    " x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3141: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.loc._setitem_with_indexer((slice(None), indexer), value)\n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3113: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_array(key, value)\n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PagePopularitylikes</th>\n",
       "      <th>PageCheckin</th>\n",
       "      <th>Pagetalking about</th>\n",
       "      <th>PageCategory</th>\n",
       "      <th>MinCC1</th>\n",
       "      <th>MaxCC1</th>\n",
       "      <th>AverageCC1</th>\n",
       "      <th>MedianCC1</th>\n",
       "      <th>SDCC1</th>\n",
       "      <th>MinCC2</th>\n",
       "      <th>...</th>\n",
       "      <th>PostpublishedFriday</th>\n",
       "      <th>PostpublishedSaturday</th>\n",
       "      <th>BaseDateTimeSunday</th>\n",
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       "      <th>BaseDateTimeWednesday</th>\n",
       "      <th>BaseDateTimeThursday</th>\n",
       "      <th>BaseDateTimeFriday</th>\n",
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       "      <th>TargetVariable</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>29364.000000</td>\n",
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       "      <td>29364.000000</td>\n",
       "      <td>29364.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002829</td>\n",
       "      <td>0.004257</td>\n",
       "      <td>0.007459</td>\n",
       "      <td>0.220561</td>\n",
       "      <td>0.000973</td>\n",
       "      <td>0.158499</td>\n",
       "      <td>0.029935</td>\n",
       "      <td>0.019132</td>\n",
       "      <td>0.068875</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148345</td>\n",
       "      <td>0.140546</td>\n",
       "      <td>0.147562</td>\n",
       "      <td>0.134995</td>\n",
       "      <td>0.134212</td>\n",
       "      <td>0.145757</td>\n",
       "      <td>0.148788</td>\n",
       "      <td>0.143305</td>\n",
       "      <td>0.145382</td>\n",
       "      <td>0.003662</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <td>0.019493</td>\n",
       "      <td>0.020005</td>\n",
       "      <td>0.189052</td>\n",
       "      <td>0.012151</td>\n",
       "      <td>0.178345</td>\n",
       "      <td>0.047375</td>\n",
       "      <td>0.038045</td>\n",
       "      <td>0.084448</td>\n",
       "      <td>0.008003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355448</td>\n",
       "      <td>0.347559</td>\n",
       "      <td>0.354671</td>\n",
       "      <td>0.341724</td>\n",
       "      <td>0.340886</td>\n",
       "      <td>0.352868</td>\n",
       "      <td>0.355885</td>\n",
       "      <td>0.350389</td>\n",
       "      <td>0.352492</td>\n",
       "      <td>0.018053</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000086</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.076190</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015745</td>\n",
       "      <td>0.003011</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.008339</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>75%</th>\n",
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       "      <td>0.038001</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>max</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PagePopularitylikes   PageCheckin  Pagetalking about  PageCategory  \\\n",
       "count         29364.000000  29364.000000       29364.000000  29364.000000   \n",
       "mean              0.002829      0.004257           0.007459      0.220561   \n",
       "std               0.012860      0.019493           0.020005      0.189052   \n",
       "min               0.000000      0.000000           0.000000      0.000000   \n",
       "25%               0.000086      0.000000           0.000110      0.076190   \n",
       "50%               0.000636      0.000000           0.001202      0.161905   \n",
       "75%               0.002753      0.000090           0.007883      0.295238   \n",
       "max               1.000000      1.000000           1.000000      1.000000   \n",
       "\n",
       "             MinCC1        MaxCC1    AverageCC1     MedianCC1         SDCC1  \\\n",
       "count  29364.000000  29364.000000  29364.000000  29364.000000  29364.000000   \n",
       "mean       0.000973      0.158499      0.029935      0.019132      0.068875   \n",
       "std        0.012151      0.178345      0.047375      0.038045      0.084448   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.015745      0.003011      0.001235      0.008339   \n",
       "50%        0.000000      0.087124      0.012912      0.006500      0.035722   \n",
       "75%        0.000000      0.255773      0.038001      0.021841      0.106069   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "             MinCC2       ...        PostpublishedFriday  \\\n",
       "count  29364.000000       ...               29364.000000   \n",
       "mean       0.000191       ...                   0.148345   \n",
       "std        0.008003       ...                   0.355448   \n",
       "min        0.000000       ...                   0.000000   \n",
       "25%        0.000000       ...                   0.000000   \n",
       "50%        0.000000       ...                   0.000000   \n",
       "75%        0.000000       ...                   0.000000   \n",
       "max        1.000000       ...                   1.000000   \n",
       "\n",
       "       PostpublishedSaturday  BaseDateTimeSunday  BaseDateTimeMonday  \\\n",
       "count           29364.000000        29364.000000        29364.000000   \n",
       "mean                0.140546            0.147562            0.134995   \n",
       "std                 0.347559            0.354671            0.341724   \n",
       "min                 0.000000            0.000000            0.000000   \n",
       "25%                 0.000000            0.000000            0.000000   \n",
       "50%                 0.000000            0.000000            0.000000   \n",
       "75%                 0.000000            0.000000            0.000000   \n",
       "max                 1.000000            1.000000            1.000000   \n",
       "\n",
       "       BaseDateTTuesday  BaseDateTimeWednesday  BaseDateTimeThursday  \\\n",
       "count      29364.000000           29364.000000          29364.000000   \n",
       "mean           0.134212               0.145757              0.148788   \n",
       "std            0.340886               0.352868              0.355885   \n",
       "min            0.000000               0.000000              0.000000   \n",
       "25%            0.000000               0.000000              0.000000   \n",
       "50%            0.000000               0.000000              0.000000   \n",
       "75%            0.000000               0.000000              0.000000   \n",
       "max            1.000000               1.000000              1.000000   \n",
       "\n",
       "       BaseDateTimeFriday  BaseDateTimeSaturday  TargetVariable  \n",
       "count        29364.000000          29364.000000    29364.000000  \n",
       "mean             0.143305              0.145382        0.003662  \n",
       "std              0.350389              0.352492        0.018053  \n",
       "min              0.000000              0.000000        0.000000  \n",
       "25%              0.000000              0.000000        0.000000  \n",
       "50%              0.000000              0.000000        0.000000  \n",
       "75%              0.000000              0.000000        0.001404  \n",
       "max              1.000000              1.000000        1.000000  \n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scalerX = MinMaxScaler(feature_range=(0, 1))\n",
    "x_train[x_train.columns] = scalerX.fit_transform(x_train[x_train.columns])\n",
    "x_test[x_test.columns] = scalerX.transform(x_test[x_test.columns])\n",
    "sns.distplot(x_train['PageCategory'])\n",
    "x_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 54 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train.plot(kind='box', subplots=True,layout=(9,6), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementing SVM for different kernels\n",
    "score = []\n",
    "accuracy = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "for kernel in ('linear', 'poly', 'rbf','sigmoid'):\n",
    "    svclassifier = SVC(kernel=kernel)  \n",
    "    model1 = svclassifier.fit(x_train, y_train)  \n",
    "    y_pred = svclassifier.predict(x_test)\n",
    "    score.append(svclassifier.score(x_test, y_test))\n",
    "    precision.append(precision_score(y_test,y_pred))\n",
    "    accuracy.append(accuracy_score(y_test,y_pred))\n",
    "    recall.append(recall_score(y_test,y_pred))\n",
    "    f1.append(f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7999205403257846,\n",
       " 0.5510528406833533,\n",
       " 0.7709972189114025,\n",
       " 0.7632101708382996]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7710492816875795,\n",
       " 0.010161177295024526,\n",
       " 0.7380000000000001,\n",
       " 0.7310954701317453]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7971423199849595, 1.0, 0.7628265363653448, 0.7497686470479363]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7466103187180841,\n",
       " 0.005106532840288783,\n",
       " 0.7147385103011094,\n",
       " 0.713329811586547]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "svclassifier_linear = SVC(kernel='linear')\n",
    "model2 = svclassifier_linear.fit(x_train, y_train) \n",
    "y_pred = svclassifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7999205403257846"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svclassifier_linear.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cm_svm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f, ax = plt.subplots(figsize =(5,5))\n",
    "sns.heatmap(cm_svm,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
    "plt.title(\"Test for Test Dataset\")\n",
    "plt.xlabel(\"predicted y values\")\n",
    "plt.ylabel(\"real y values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7995164146574036"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svclassifier_linear.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_svm_train = confusion_matrix(y_train,svclassifier.predict(x_train))\n",
    "f, ax = plt.subplots(figsize =(5,5))\n",
    "sns.heatmap(cm_svm_train,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
    "plt.xlabel(\"predicted y values\")\n",
    "plt.ylabel(\"real y values\")\n",
    "plt.title(\"Test for Train Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using k fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7952509426097129"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[x.columns] = scalerX.fit_transform(x[x.columns])\n",
    "svclassifier = SVC(kernel='linear')  \n",
    "cross_val_score(svclassifier, x, y, scoring='accuracy', cv = 5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision tree for Facebook Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  1.0\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(criterion = \"entropy\")\n",
    "dtc.fit(x_train,y_train)\n",
    "print(\"score: \", accuracy_score(y_test,dtc.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.9779102105681367\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(criterion = \"gini\",splitter = 'random', max_depth = i)\n",
    "dtc.fit(x_train,y_train)\n",
    "print(\"score: \", accuracy_score(y_test,dtc.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.6815256257449345\n",
      "score:  0.690186730234406\n",
      "score:  0.7615415176797775\n",
      "score:  0.7383392928088995\n",
      "score:  0.8175605880015892\n",
      "score:  0.8529996027016289\n",
      "score:  0.8532379817242749\n",
      "score:  0.8452920143027414\n",
      "score:  0.8707191100516488\n",
      "score:  0.9565355582042113\n",
      "score:  0.9736988478347238\n",
      "score:  0.9735399284862932\n",
      "score:  0.9601112435439014\n",
      "score:  0.9783869686134287\n",
      "score:  0.9841080651569328\n",
      "score:  0.9823599523241955\n",
      "score:  0.9896702423520064\n",
      "score:  0.9864123957091776\n",
      "score:  0.9827572506952722\n"
     ]
    }
   ],
   "source": [
    "# Experimenting with pruning\n",
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= i)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"entropy\",splitter = 'random', max_depth = i)\n",
    "    dtc.fit(x_train,y_train)\n",
    "    print(\"score: \", accuracy_score(y_test,dtc.predict(x_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.6693682955899881\n",
      "score:  0.6864521255462852\n",
      "score:  0.7234008740564164\n",
      "score:  0.7712355979340485\n",
      "score:  0.8715137067938021\n",
      "score:  0.8041319030591975\n",
      "score:  0.886849423917362\n",
      "score:  0.8363925307906238\n",
      "score:  0.9213349225268176\n",
      "score:  0.919745729042511\n",
      "score:  0.932777115613826\n",
      "score:  0.9715534366309098\n",
      "score:  0.9589988081048868\n",
      "score:  0.9755264203416766\n",
      "score:  0.97632101708383\n",
      "score:  0.976797775129122\n",
      "score:  0.9723480333730632\n",
      "score:  0.977036154151768\n",
      "score:  0.9841080651569328\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= i)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"gini\",splitter = 'random', max_depth = i)\n",
    "    dtc.fit(x_train,y_train)\n",
    "    print(\"score: \", accuracy_score(y_test,dtc.predict(x_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.7873158231902626\n",
      "score:  0.9135169762972454\n",
      "score:  0.8340807174887892\n",
      "score:  0.8846893017296604\n",
      "score:  0.9929532351057014\n",
      "score:  0.9891095451633568\n",
      "score:  0.9788597053171044\n",
      "score:  0.9929532351057014\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= i)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"entropy\",splitter = 'random', max_depth = i)\n",
    "    dtc.fit(x_train,y_train)\n",
    "    print(\"score: \", accuracy_score(y_test,dtc.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(criterion = \"entropy\")\n",
    "dtc.fit(x_train,y_train)\n",
    "print(\"score: \", accuracy_score(y_test,dtc.predict(x_test)))\n",
    "cm_dtc = confusion_matrix(y_test,dtc.predict(x_test))\n",
    "f, ax = plt.subplots(figsize =(5,5))\n",
    "sns.heatmap(cm_dtc,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
    "plt.title(\"Test for Test Dataset\")\n",
    "plt.xlabel(\"predicted y values\")\n",
    "plt.ylabel(\"real y values\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score:  1.0\n",
      "precision_score:  1.0\n",
      "recall_score:  1.0\n",
      "f1_score:  1.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"accuracy_score: \", accuracy_score(y_test,dtc.predict(x_test)))\n",
    "print(\"precision_score: \", precision_score(y_test,dtc.predict(x_test)))\n",
    "print(\"recall_score: \", recall_score(y_test,dtc.predict(x_test)))\n",
    "print(\"f1_score: \",f1_score(y_test,dtc.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting for Facebook Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AdaBoost = AdaBoostClassifier(base_estimator= dtc,learning_rate=1)\n",
    "AdaBoost.fit(x_train,y_train)\n",
    "prediction = AdaBoost.score(x_test,y_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.8090584028605483\n",
      "score:  0.8833531982518872\n",
      "score:  0.901787842669845\n",
      "score:  0.9315057608263806\n",
      "score:  0.99880810488677\n",
      "score:  0.9966626936829559\n",
      "score:  0.9969010727056019\n",
      "score:  0.9902264600715137\n",
      "score:  0.9889551052840684\n",
      "score:  0.9825983313468415\n",
      "score:  0.9832340087405642\n",
      "score:  0.9864123957091776\n",
      "score:  0.9911005164878824\n",
      "score:  0.9858561779896703\n",
      "score:  0.9863329360349623\n",
      "score:  0.9884783472387764\n",
      "score:  0.9889551052840684\n",
      "score:  0.9868096940802543\n",
      "score:  0.9851410409217323\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= i)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"entropy\",splitter = 'random', max_depth = i)\n",
    "    AdaBoost = AdaBoostClassifier(base_estimator= dtc,learning_rate=1)\n",
    "    AdaBoost.fit(x_train,y_train)\n",
    "    prediction = AdaBoost.score(x_test,y_test)\n",
    "    prediction\n",
    "    print(\"score: \", accuracy_score(y_test,AdaBoost.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset 2 - Admission Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Work Exp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Volunteer/Leadership</th>\n",
       "      <th>Sports</th>\n",
       "      <th>Certificates/Awards</th>\n",
       "      <th>Languages</th>\n",
       "      <th>NGO</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Work Exp  Age  Volunteer/Leadership  Sports  Certificates/Awards  \\\n",
       "0         1         8   29                     1       1                   21   \n",
       "1         1         0   29                     1       1                   25   \n",
       "2         1        10   23                     1       1                   13   \n",
       "3         1         8   22                     1       1                   16   \n",
       "4         0         6   20                     0       1                    4   \n",
       "\n",
       "   Languages  NGO  Chance of Admit   \n",
       "0          2    1              0.92  \n",
       "1          3    1              0.76  \n",
       "2          5    1              0.72  \n",
       "3          6    1              0.80  \n",
       "4          2    1              0.65  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset2 = pd.read_csv(\"Admission_Predict.csv\")\n",
    "Dataset2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5203, step=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset2 = pd.DataFrame(Dataset2)\n",
    "Dataset2.columns = Dataset2.columns.to_series().apply(lambda x: x.strip())\n",
    "Dataset2['Admit'] = [1 if each > 0.60 else 0 for each in Dataset2['Chance of Admit']]\n",
    "Dataset2.reset_index()\n",
    "y2 = Dataset2['Admit'].values\n",
    "x2 = Dataset2.drop(['Admit'],axis = 1)\n",
    "N_rows = Dataset2.shape[0] \n",
    "Dataset2.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b80b6bc0f0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Dataset2['GRE Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(Dataset2.corr(), ax=ax, annot=True, linewidths=0.05, fmt= '.2f',cmap=\"Reds\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Having Research: 2574\n",
      "Having Research: 2629\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Not Having Research:\",len(Dataset2[Dataset2.Research == 0]))\n",
    "print(\"Having Research:\",len(Dataset2[Dataset2.Research == 1]))\n",
    "y = np.array([len(Dataset2[Dataset2.Research == 0]),len(Dataset2[Dataset2.Research == 1])])\n",
    "x = [\"Not Having Research\",\"Having Research\"]\n",
    "plt.bar(x,y)\n",
    "plt.title(\"Research Experience\")\n",
    "plt.xlabel(\"Canditates\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array([Dataset2[\"TOEFL Score\"].min(),Dataset2[\"TOEFL Score\"].mean(),Dataset2[\"TOEFL Score\"].max()])\n",
    "x = [\"Worst\",\"Average\",\"Best\"]\n",
    "plt.bar(x,y)\n",
    "plt.title(\"TOEFL Scores\")\n",
    "plt.xlabel(\"Level\")\n",
    "plt.ylabel(\"TOEFL Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Dataset2[\"GRE Score\"].plot(kind = 'hist',bins = 200,figsize = (6,6))\n",
    "plt.title(\"GRE Scores\")\n",
    "plt.xlabel(\"GRE Score\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = Dataset2[Dataset2[\"Chance of Admit\"] >= 0.75][\"University Rating\"].value_counts().head(5)\n",
    "plt.title(\"University Ratings of Candidates with an 75% acceptance chance\")\n",
    "s.plot(kind='bar',figsize=(20, 10))\n",
    "plt.xlabel(\"University Rating\")\n",
    "plt.ylabel(\"Candidates\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Dataset2[\"University Rating\"],Dataset2.CGPA)\n",
    "plt.title(\"CGPA Scores for University Ratings\")\n",
    "plt.xlabel(\"University Rating\")\n",
    "plt.ylabel(\"CGPA\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b80b6cfc18>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x2_train, x2_test, y2_train, y2_test = train_test_split(x2, y2, test_size = 0.30)\n",
    "\n",
    "scalerX = MinMaxScaler(feature_range=(0, 1))\n",
    "x2_train[x2_train.columns] = scalerX.fit_transform(x2_train[x2_train.columns])\n",
    "x2_test[x2_test.columns] = scalerX.transform(x2_test[x2_test.columns])\n",
    "sns.distplot(x2_train['GRE Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishi\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b80b64e7b8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x2_train['Work Exp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 25\n",
    "x2_train.plot(kind='box', subplots=True,layout=(5,6), sharex=False, sharey=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM for Admission Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementing SVM for different kernels\n",
    "from sklearn.svm import SVC\n",
    "score = []\n",
    "accuracy = []\n",
    "precision = []\n",
    "recall = []\n",
    "f1 = []\n",
    "for kernel in ('linear', 'poly', 'rbf','sigmoid'):\n",
    "    svclassifier = SVC(kernel=kernel)  \n",
    "    model1 = svclassifier.fit(x2_train, y2_train)  \n",
    "    y2_pred = svclassifier.predict(x2_test)\n",
    "    score.append(svclassifier.score(x2_test, y2_test))\n",
    "    precision.append(precision_score(y2_test,y2_pred))\n",
    "    accuracy.append(accuracy_score(y2_test,y2_pred))\n",
    "    recall.append(recall_score(y2_test,y2_pred))\n",
    "    f1.append(f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.985906470211403, 0.9301729660474055, 0.9750160153747598, 0.968609865470852]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9785522788203753,\n",
       " 0.9644444444444444,\n",
       " 0.9628154050464808,\n",
       " 0.9549668874172186]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9851551956815114,\n",
       " 0.9227498228206944,\n",
       " 0.9738079247817326,\n",
       " 0.9671361502347419]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9918478260869565,\n",
       " 0.8845108695652174,\n",
       " 0.9850543478260869,\n",
       " 0.9796195652173914]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "svclassifier = SVC(kernel=\"linear\")  \n",
    "model2 = svclassifier.fit(x2_train, y2_train) \n",
    "y_pred = svclassifier.predict(x2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_svm = confusion_matrix(y2_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mrgjNLO/q+LubnAjNrEycCM0s7+q5IvSDWc0s99pNhJKGSuqeXu8h6ZuS+lQ+NDOrK40lTjWgmIrwVmCFpE2Bq4AhwO8rGpWZ1Z1oLG2qBcWcI2yMiOWSDgN+FRG/kfRkpQMzs/pSK0mtFMUkwmWSjgGOA0aked0qF5KZ1aN6ToTFdI2PB3YGzo+IlyQNAW6obFhmVndCpU01oN2KMCKekXQm8LH0/iXggkoHZmb1pVNXhJJGAFOBu9P74ZJur3RgZlZfolElTbWgmK7xj4AdgXkAETGVbOTYzGylzj5qvDwi5ksfyNz1+5gJM6uIqJHzfaUoJhFOl/QloIukYcA3gUcrG5aZ1Ztaqe5KUUzX+FRgK2AJcCOwAPhWJYMys/pTz+cIixk1Xgj8ME1mZi2KOj5h1m4ilPQgLZwTjIi9KhKRmdWlWqnuSlHMOcLTC173AL4ALK9MOGZWryqZCCV1AaYAMyPiIEnXArsD89MqIyNiqrJR3UuBA4GFaf4T7e2/mK7x481mPSLpb6twDGaWAxXuGp8GPAv0Kpj3vYgY32y9A4Bhafo0cFn62aZiLqjuWzD1k7QfsEGx0ZtZPlRqsETShsDngSuLCOMQYExkJgJ9JA1ob6NiusaPk50jFFmX+CXghCK2MzMrh18BZwBrN5t/vqRzgPuB70fEEmAQ8FrBOg1p3httNVBM19h3kZhZu0q9oFrSKGBUwazRETE6LTsIeDMiHpe0R8E6ZwH/BlYHRgNnAj8hK9g+FFp7MbSaCCUd3taGEfGH9nZuZvlR6gXVKemNbmXxrsDBkg4kG6ztJemGiDg2LV8i6RreH9RtAAYXbL8h8Hp7MbRVEY5oY1kAToRmtlJjBW6xi4izyKo/UkV4ekQcK2lARLyRRokPBaanTW4HTpE0jmyQZH5EtNkthjYSYUQc/xGPwcxypIPvNR4rqT9ZV3gq8J9p/p1kl87MILt8pqg8VtTXeUr6PNltdj2a5kXET4qP2cw6u0pfUB0RE4AJ6XWLN3RERAAnr+q+i7mz5HJgTWBPsuHrI4DHVrUhM+vc6vkWu2IeurBLRHwFmBsRPyZ7bP/gdrYxs5zp1A9dABalnwslDQTewg9mNbNmKjFY0lGKSYR3pC90/wXwBNmI8e8qGpWZ1Z1O/WDWiDgvvbxV0h1Aj4iY39Y2ZpY/9XyOsJjBkv8DbgJuiogXyB7Qamb2AfXcNS5msORgsnuMb5Y0WdLpkj5W4bjMrM5EqKSpFrSbCCPilYi4MCK2B74EbE324AUzs5UiSptqQbEXVG8MfBE4ClhB9iSIiuvWf2hHNGMVsmRJQ7VDsA5Uz13jYs4RTgK6ATcDR0bEixWPyszqTq10c0tRTEV4XEQ8V/FIWtC128BqNGsf0fJl2cM+Fj/6+ypHYqXo0f4qLarnirCYc4RVSYJmZh2lqHOEZmbtqZFxj5I4EZpZWXTqrrGkKZJOlrRORwRkZvWpU19HCBwNDAQmSxonab/0VFgzs5UaS5xqQTGDJTMi4ofAZsDvgauBVyX9WFLfSgdoZvUhUElTLSimIkTS1sDFZE+guZXs4awLgAcqF5qZ1ZPGKG2qBcVcUP04MA+4ive/OxRgkqRdKxmcmdWPxhqp7kpRzKhxq3eTRESbX/lpZvlRK93cUhTzPELfUmdm7aqVgY9S+DpCMyuLTl0RmpkVo1NWhJLaPP8XEX8ofzhmVq86ZSIERrSxLAAnQjNbqVN2jSPi+I4MxMzqW418RXFJin1C9eeBrSh4VFlE/KRSQZlZ/enU1xFKuhxYE9gTuJLsrpLHKhyXmdWZGrlJpCTF3GK3S0R8BZgbET8GdgYGVzYsM7OOU0zXeFH6uVDSQOAtYEjlQjKzetRZR42b3CGpD9kDF54gq4CvrGhUZlZ3Guv46XzF3GJ3Xnp5q6Q7gB4RMb+yYZlZvenU5wglrSnpbEm/S0+eWU/SQR0Qm5nVkU79YFbgGmAJ2SAJQAPw04pFZGZ1qVGlTbWgmEQ4NCIuBJYBRMQiqOMLhsysIhpRSVMtKGawZKmkNUinACQNJasQzcxWqudzhMUkwnOBu4HBksYCuwIjKxmUmdWfWunmlqLNRJi+re454HBgJ7Iu8WkRMacDYjOzOlIrAx+laDMRRkRI+lNEbA/8pYNiMrM6VM9d42IGSyZK+lTFIzGzulbPo8bFnCPcE/i6pFeA98i6xxERW1c0MjOrK522a5wcUPEozKzuVSoRSuoB/B3oTpazxkfEuZKGAOOAvmS3/345IpZK6g6MAbYnezbCURHxcltttNs1johXWpo+0pGZWacTKm0qwhJgr4jYBhgO7C9pJ+DnwCURMQyYC5yQ1j+B7GlZmwKXpPXaVMw5QjOzdlXqFrvIvJvedktTAHsB49P864BD0+tD0nvS8r3TFTCtciI0s7Ko5L3GkrpImgq8CdwLvADMi4jlaZUGYFB6PQh4DSAtnw+s29b+nQjNrCyixEnSKElTCqZRH9p3xIqIGA5sCOwIbNlKCNDyLcBtXt3j7zU2s6qKiNHA6CLXnSdpAtkNHn0kdU1V34bA62m1BrKn6DdI6gr0Bt5ua7+uCM2sLCp1HaGk/unh0KTnHuwDPAs8SPYdSgDHAbel17en96TlD0SEK0Izq7wKXkc4ALhOUhey4u3miLhD0jPAOEk/BZ4ErkrrXwVcL2kGWSV4dHsNOBGaWVlUKhFGxDRg2xbmv0h2vrD5/MXAkavShhOhmZVFPd9r7ERoZmVRK/cNl8KJ0MzKorPfa2xm1i53jc0s9xrrOBU6EZpZWbhrbGa5V7/1oBOhmZWJK0Izyz1fPmNmuefBEjPLvfpNg06EZlYmPkdoZrlXz11jP4/QzHLPFaGZlUX91oNOhGZWJj5HaGa5V8/nCJ0Izaws6jcNOhGaWZm4a2xmuRd1XBM6EZpZWbgiNLPc82CJrZLevXsx+oqL2GqrzYkIvva17zJx0uPVDsuSl9+Ywxm/vWXl+4bZcznpsD2Z9+4iJjz5HKtJrNNrLc478VDWW6cXEcHPx97Fw9P+RY/Vu3HeiYey5cYDq3gE1VG/adCJsCou+eVPuOeeBznq6FF069aNNddco9ohWYGNB/Tj5vO+AcCKxkY+962L2Wv7Lem1Vg9O+cJeAIy9dyJX3PY3zh45goen/YtXZ73Nn3/+TZ56oYGfjvkLY8/5WjUPoSrquSL0LXYdbO21e7LbZz7N1dfcCMCyZcuYP39BlaOy1kx65kUGr9eXgf360HONHivnL16yDCl7AN+DTz7PiF23QRJbbzqYdxYuZva8d6oVctU0ljjVgg5PhJKO7+g2a8kmm2zEnDlvcdWVlzD5sXu44vJfuCKsYXdPms7+O31i5fvfjL+ffb/zS/7yj2mcdNieALw5dwHr9+21cp311+nFm3Pz98ctSvyvFlSjIvxxFdqsGV27dGHbbT/JFVeM4VM77sd77y3kzDNOqXZY1oJly5fztyefZ99PbbVy3qlH7M1ff/kdPr/z1oy7/7FsZguf5aZqMU9cETYjaVor01PA+m1sN0rSFElTRlcisBrQMPMNGhre4LHJTwLwhz/8hW2Hf7LKUVlLHp42gy02GsC6vXt+aNkBO32S+6Y8A8B6fXsx6+33K8BZcxfQv8/aHRZnrajnirBSgyXrA/sBc5vNF/BoaxtFxGggy4FSnFSh4Kpp1qzZNDS8zmabDeWf/3yBvfb6DM8++89qh2UtuGviUxyw0/t/pF7591tstMG6AEx48nmGDOgHwB7DN2fc/Y+x/6c/wVMvNNBzje65TIS1Ut2VolKJ8A6gZ0RMbb5A0oQKtVk3Tvv22Yy57jesvno3XnrpVU448TvVDsmaWbRkKROffpGzR45YOe/SW+7j5X/PYTWJAev24b9GHgTAbtsM4+Fp/+KgM35Nj+7d+MkJh1Qr7KpqjNqo7kqhqNXgpejaLX/XYnUGy5e9DsDiR39f5UisFD12PqakE5xf3ujwkpLJ9a/8oeonVH0doZmVRY2WVEVxIjSzsqjnC6qdCM2sLGplBLgUToRmVhYeNTaz3HPX2Mxyz11jM8s9d43NLPdq9prkIjgRmllZ+ByhmeWeu8Zmlnv1PFjiJ1SbWVk0EiVN7ZF0taQ3JU0vmPcjSTMlTU3TgQXLzpI0Q9LzkvYrJnZXhGZWFhUcLLkW+B9gTLP5l0TERYUzJH0cOBrYChgI3Cdps4hY0VYDrgjNrCwq9YTqiPg78HaRYRwCjIuIJRHxEjAD2LG9jZwIzawsqvCE6lPSk++vlrROmjcIeK1gnYY0r01OhGZWFqWeIyz8io40jSqiucuAocBw4A3g4jS/pWcbtpttfY7QzKrqA1/RUfw2s5peS/od2VPxIasABxesuiHwenv7c0VoZmURESVNpZA0oODtYUDTiPLtwNGSuksaAgwDHmtvf64IzawsKnVniaQbgT2AfpIagHOBPSQNJ+v2vgx8HSAinpZ0M/AMsBw4ub0RY3AiNLMyqdQF1RFxTAuzr2pj/fOB81elDSdCMyuLev4WOydCMyuL+k2DToRmViZ++oyZ5Z4ToZnlnh/Mama554rQzHKvnp9H6ERoZmXhrrGZ5Z67xmaWe64IzSz3XBGaWe55sMTMcq+e7zX28wjNLPdcEZpZWbhrbGa5V89dYydCMysLV4RmlnuuCM0s91wRmlnuuSI0s9xzRWhmuRfRWO0QSuZEaGZl4XuNzSz3/PQZM8s9V4RmlnuuCM0s93z5jJnlni+fMbPcc9fYzHLPgyVmlnv1XBH6CdVmlnuuCM2sLDxqbGa5V89dYydCMysLD5aYWe65IjSz3PM5QjPLPd9ZYma554rQzHLP5wjNLPfcNTaz3HNFaGa5V8+JUDUbvFSjgZl1chEqZbOuqw8q6TO7fOnMktorp9pNhJ2cpFERMbracVhp/PvrXPz0meoZVe0A7CPx768TcSI0s9xzIjSz3HMirB6fX6pv/v11Ih4sMbPcc0VoZrnnRFgFkvaX9LykGZK+X+14rHiSrpb0pqTp1Y7FyseJsINJ6gL8L3AA8HHgGEkfr25UtgquBfavdhBWXk6EHW9HYEZEvBgRS4FxwCFVjsmKFBF/B96udhxWXk6EHW8Q8FrB+4Y0z8yqxImw47V0X6WH7s2qyImw4zUAgwvebwi8XqVYzAwnwmqYDAyTNETS6sDRwO1Vjsks15wIO1hELAdOAe4BngVujoinqxuVFUvSjcA/gM0lNUg6odox2UfnO0vMLPdcEZpZ7jkRmlnuORGaWe45EZpZ7jkRmlnuORHmkKR308+Bksa3s+63JK25ivvfQ9IdHyXGWmzLOi8nwk4iPdVmlUTE6xFxRDurfQtYpURoVm+cCGucpI0lPSfpOknTJI1vqtAkvSzpHEkPA0dKGirpbkmPS3pI0hZpvSGS/iFpsqTzmu17enrdRdJFkp5K7Zwq6ZvAQOBBSQ+m9fZN+3pC0i2Seqb5+6c4HwYOb+VYHpI0vOD9I5K2brbOJElbFbyfIGl7STtKelTSk+nn5i3s/0eSTi94P13Sxun1sZIekzRV0hXpeLtIujat95Skb6/ab8c6CyfC+rA5MDoitgYWACcVLFscEZ+JiHFk36NxakRsD5wO/DatcylwWUR8Cvh3K22MAoYA26Z2xkbEr8nug94zIvaU1A/4L2CfiNgOmAJ8R1IP4HfACGA3YINW2rgSGAkgaTOge0RMa7bOOOCLaZ0BwMCIeBx4DvhsRGwLnAP8rPV/rg+StCVwFLBrRAwHVgD/AQwHBkXEJyLik8A1xe7TOhcnwvrwWkQ8kl7fAHymYNlNAKky2wW4RdJU4ApgQFpnV+DG9Pr6VtrYB7g83QJIRLT0zL2dyB4m+0hq4zhgI2AL4KWI+Fdktyrd0EobtwAHSeoGfJXsIafN3QwcmV5/MW0D0Dsd23TgEmCrFrZtzd7A9sDkFPfewCbAi8Amkn4jaX+yPzKWQ12rHYAVpfl9kIXv30s/VwPmpYqnmH00pyLXuTcijvnAzKy72+69mhGxUNK9ZA+i/SKwQwvrzJT0VuoyHwV8PS06D3gwIg5L3d0JLTSxnA/+ce9REPd1EXHWhw5I2gbYDzg5xfTV9o7DOh9XhPXhY5J2Tq+PAR41OLUUAAABV0lEQVRuvkJELABeknQkgDLbpMWPkD3lBrIuYUv+CvynpK5p+75p/jvA2un1RGBXSZumddZMXdzngCGShhbE2JorgV8Dk1upOiHrHp8B9I6Ip9K83sDM9HpkK9u9DGyXYtuOrKsPcD9whKT1mo5N0kapq79aRNwKnN20reWPE2F9eBY4TtI0oC9wWSvr/QdwgqT/A57m/a8AOA04WdJksoTSkiuBV4FpafsvpfmjgbskPRgRs8mS0I0plonAFhGxmOwc41/SYMkrrR1IOt+3gLbPx40nS9w3F8y7EPhvSY8ArY2Q3wr0Td3fbwD/TG0+Q3Zu868p7nvJThsMAiak9a8FPlQxWj746TM1LnUD74iIT1Q5lLKQNJCsW7tFRDRWORwzwBWhdSBJXwEmAT90ErRa4orQzHLPFaGZ5Z4ToZnlnhOhmeWeE6GZ5Z4ToZnlnhOhmeXe/wNHt/xfTKdx7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize =(5,5))\n",
    "sns.heatmap(cm_svm,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
    "plt.title(\"Test for Test Dataset\")\n",
    "plt.xlabel(\"predicted y values\")\n",
    "plt.ylabel(\"real y values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/OIOfnfq/DF9z6NL9B60+kMeemMkHCxYQEUx9eDprrzUMq54Jvz+HmTNncf75Fy0tu+mmW9kh/REcNWoEPbr3YG76Q2j1Q1GhoXlJXchm0RhCdv+xAZjW/NBmESeIxXOerUjbKuHRfz/BoUf+L6NGDqeLsr87x35jHBttsD7fO+WnvDZ7DoNWH8h5Z55Mv74rcerPfsXkfzzAoNVXA6Br165cc8kFAFx48eXcdue9dO3alfXXHcnpJx5Ljx4d5zGf7gNHAtCjR5tzAdS8MWO25J67/8zjjz9FU8p2Tjn159x5531cNOFcNt54NIsWLeaEE8/gnnv+WeXWlseihQ0tvc/cpvd+cmhJwaTPyZeVVF97qFiA/MQ6WIC0D9VTgOyMSg6QZx5SWoD84RU1GyD9HKSZlUcdDtI4QJpZedT4gEspHCDNrDycQZqZ5ajxh75L4QBpZuXhDNLMrGW1/tB3KRwgzaw8nEGameVwgDQzy+FBGjOzHM4gzcxaFg6QZmY5HCDNzHL4MR8zsxzOIM3MctRhgKzYtxqamXV0ziDNrCxqdvLtT8AB0szKow672A6QZlYeDpBmZi3zg+JmZnkcIM3MctTfc+IOkGZWHu5im5nlcYA0M8vhLraZWcvcxTYzy+MM0sysZc4gzczyOIM0M2tZHX5nlwOkmZWJA6SZWcvqMYP0hLlmZjnaDJCSRkrqmda3l3SMpP6Vb5qZdShNJS41rJgM8npgiaR1gInACOBPFW2VmXU40VTaUsuKCZBNEdEIfAn4VUR8BxhU2WaZWUdTqQAp6RJJb0h6oqDsF5JmSnpM0p8Le7WSTpI0S9LTknYtKN8tlc2SdGIx11RMgFws6SBgHHBzKutezMnNrPOoYAZ5KbDbMmV3ABtGxEbAf4CTACSNBg4ENkjH/J+krpK6Ar8BdgdGAwelfVtVTIA8HNga+ElEPC9pBHBFMVdlZp1IqLSlrdNG3AvMW6bs9tSzBZgCDE3rY4FJEbEwIp4HZgGfScusiHguIhYBk9K+rWrzMZ+IeFLSCcCa6fPzwFltXpWZdSpVvJ/4NeDqtD6ELGA2a0hlAC8vU/7Ztk5czCj23sB04Nb0eRNJN7XdZjPrTKJJJS2Sxkt6uGAZX2ydkk4GGoErm4taalor5a0q5kHxH5Olp/cARMT01M02M1uq1AwyIiYAE5b3OEnjgL2AneLDL+VuAIYV7DYUeDWt55XnKuYeZGNEvL1MWf1N22Fmn0iESlpKIWk34ATgCxHxfsGmm4ADJfVMidwo4CFgGjBK0ghJPcgGctrsCReTQT4h6StAV0mjgGOAfy7f5ZhZvavUPUhJVwHbA6tKagB+RDZq3RO4QxLAlIj4ZkTMkHQN8CRZ1/uoiFiSznM0cBvQFbgkIma0WfeHmWlu43oDJwO7kPXjbwPOiIgFJVxr8aRYPOfZilZhldF94EgAevQY0saeVosWLWwoKa17ecudSupZDpt2Z2lpZDsoZhT7fbIAeXLlm2NmHVUbuVaH1GaAlHQ3LdxzjIgdK9IiM+uQoqlmE8GSFXMP8viC9V7Al8n69mZmS3XKABkRjyxT9ICkf1SoPWbWQXXWLvaAgo9dgM2BNSrWIjPrkDplBgk8wodPojcCzwNHVLJRZma1oJgutt+aMbM2lfrQdy3LDZCS9mntwIi4ofzNMbOOqtYnvy1Faxnk3q1sC8AB0syWaupMGWREHN6eDTGzjq1TdbELSdqTbIbeXs1lEXF6pRplZh1PpxzFlvQ7oDewA3AxsC/Z7BhmZkvV43OQxUx3NiYiDgXmR8RpZF+/MKyNY8yskyl1wtxaVkwX+4P0831Jg4E3yb761cxsqU41SFPg5vSVir8AHiUbwb6ooq0ysw6nUw7SRMQZafV6STcDvVqYYdzMOrl6vAdZzCDNv8m+MezqiHgWWFjxVplZh1OPXexiBmm+QPYO9jWSpkk6XtKaFW6XmXUw7fmdNO2lzQAZES9GxNkRsTnwFWAjsgkrzMyWiihtqWXFPig+HNgfOABYAny/ck36UPN3m1jHtGjRK9VugrWjeuxiF3MPcirQHbgG2C8inqt4q8ysw6n17nIpiskgx0XEzIq3pAXdug+uRrX2CTUuzr6P/f0Lj6pyS6wUvUs8rh4zyGLuQVYlOJqZVVtR9yDNzNpS4+MtJXGANLOy6JRdbEkPSzpK0srt0SAz65g65XOQwIHAYGCapEmSdpVU21dlZu2uqcSllhUzSDMrIk4G1gX+BFwCvCTptGW+EtbMOrFAJS21rJgMEkkbAeeSzehzPdmkue8Ad1WuaWbWkTRFaUstK+ZB8UeAt4CJwIkR0TxZxVRJ21SycWbWcTTVeDZYimJGsXPfnomIVr8a1sw6j1rvLpeimPkg/WqhmbWp1gdcSuHnIM2sLDplBmlmVoxOlUFKavX+YkTcUP7mmFlH1akCJLB3K9sCcIA0s6U6VRc7Ig5vz4aYWcdW419xXZJiZxTfE9gA6NVcFhGnV6pRZtbxdMrnICX9jmwOzR2Ai8neonmowu0ysw6mxl+KKUkxrxqOiYhDgfkRcRqwNTCsss0yM6u+YrrYH6Sf70saDLwJjKhck8ysI+pso9jNbpbUn2yiikfJMumLK9oqM+twmupwFsRiXjU8I61eL+lmoFdEvF3ZZplZR9Mp70FK6i3pFEkXpZl8VpO0Vzu0zcw6kE45YS7wB2Ah2eAMQANwZsVaZGYdUpNKW2pZMQFyZEScDSwGiIgPoA4feDKzT6QJlbQUQ9J3JM2Q9ISkqyT1kjRC0lRJz0i6WlKPtG/P9HlW2j681GsqJkAukrQC6RaDpJFkGaWZ2VJR4tIWSUOAY4AtImJDoCvZd2X9HPhlRIwC5gNHpEOOIHsscR3gl2m/khQTIH8E3AoMk3QlcCfw/VIrNLP6VOEudjdgBUndyF5ceQ3YEbgubf8j8MW0PjZ9Jm3fqdQvGmx1FDuddCawD7AVWdf62IiYW0plZla/KjXgEhGvSDoHeInsuezbgUeAtyKiMe3WAAxJ60OAl9OxjZLeBlYBljtutZpBRkQAf4mINyPibxFxs4OjmbWk1C62pPGSHi5YxheeV9LKZFnhCLKvoO4D7J7TBGh5jKSkp5CKeVB8iqQtI2JaKRWYWedQ6oh0REwAJrSyy87A8xExB0DSDcAYoL+kbimLHAq8mvZvIHsduiF1yfsB80ppWzH3IHcAHpT0rKTHJD0u6bFSKjOz+lXB5yBfArZKz2QL2Al4EribbPIcgHHAjWn9pvSZtP2u1BtebsVkkC2lsmZmH1HBe5BTJV1H9qpzI/Avsozzb8AkSWemsonpkInA5ZJmkWWOB5ZadzGvGr5Y6snNrPOICj4dHRE/InuiptBzwGda2HcBsF856vWXdplZWdT6a4OlcIA0s7JwgDQzy9EpZ/MxM+usnEGaWVnU+sw8pXCANLOy8D1IM7McDpBmZjnqcZDGAdLMysL3IM3McriLbWaWw11sM7McTXUYIh0gzaws3MU2M8tRf/mjA6SZlYkzSDOzHH7Mx8wshwdpzMxy1F94dIA0szLxPUgzsxz12MX2hLlmZjmcQZpZWdRf/ugAaWZl4nuQZmY56vEepAOkmZVF/YVHB0gzKxN3sc3MckQd5pAOkGZWFs4gzcxyeJDGyqJLly5MnXILr77yOmO/NK7azTHgx5NncO/zcxiwQg+uO2QMAL+b8iw3zHiFlVfoDsDRY9Zhu+EDWbykidPvfJKZc95lSVOw5/qDOGLLEUvPtaQpOHjSVFZbsScXfGHTqlxPNdRfeHSArIpjvv11Zs58hr4rrVTtpliy96cGc8BGwzjl9ic+Un7Ipmty6GbDP1I2edZsFi1p4tqDt+aDxUv48hX/ZPf11mBw3xUA+NP0lxgxoA/vLWpsr+bXhHrMIP2qYTsbMmQQe+y+E5dcclW1m2IFNh+yMv16dS96/wWLl9DY1MTCxiV079qFPj2yXGP2uwu4/4W5fGmDIZVqas1qKnGpZe0eICUd3t511pLzzj2NE086k6amWv+/hgFM+vfL7H/lg/x48gzeWbAYgJ3XWZ1e3bvy+YvvZfc/3Mehm621NLj+4t6nOXbbUXSpw8lj2xIl/q+WVSODPK0KddaEPffYmTfemMuj/3q82k2xIuy30VD+Om5bJn1lK1bt3ZPz7v8PADNmv0PXLuL2Iz7H3w7bjssffZGGt9/P7mH27sHo1fpWueXVUY8ZZEXuQUp6LG8TsHorx40HxgP8vgLtqrYxY7Zg7712YffddqRXr5707bsSf7z0AsYddky1m2YtWKV3z6Xr+2w4hGNu+hcAtzz9GmPWXIXuXbswoHcPNhncnydnv8PMOe/yj+fmcP8Lc1m0pIn3FjVy8m2P85NdP12tS2hXtZ4NlqJSgzSrA7sC85cpF/DPvIMiYgIwIdtTcWSFGlctJ//wLE7+4VkA/M/ntua73/mmg2MNm/PeQgb2yYLkXc++wchVVgRgjZV6Ma1hPnuuP4gFjU089trbfGWTNdll3TU4ZptRADzcMI/LHn2x0wRHqP1ssBSVCpA3AytGxPRlN0i6p0J1mpXsxFsf45GG+by1YDG7TryXb241kkca5vP03HcRMKhvL36442gADthoGD+aPIN9r3yQCBg7ejDrruonEpqi/jJIRa1elBTdug+udiusBI2LXwXg/QuPqnJLrBS9j7qwpCGmr661T0nB5PIXb6jZIS0/B2lmZVGjqdYn4gBpZmVRjw+KO0CaWVl4FNvMLIdHsc3McriLbWaWw11sM7Mc9djF9mw+ZlYWEVHSUgxJXSX9S9LN6fMISVMlPSPpakk9UnnP9HlW2j78k1yTA6SZlUUTUdJSpGOBpwo+/xz4ZUSMInul+YhUfgQwPyLWAX6Z9iuZA6SZlUWlZvORNBTYE7g4fRawI3Bd2uWPwBfT+tj0mbR9p7R/SRwgzawsKjgf5K+A7/NhPF0FeCsimqdsbwCaZygeArwMkLa/nfYviQOkmZVFqV1sSeMlPVywjG8+p6S9gDci4pGCqlrKCKOIbcvNo9hmVhalTnzzkWkOP24b4AuS9gB6AX3JMsr+krqlLHEo8GravwEYBjRI6gb0A+aV1DCcQZpZmVTiHmREnBQRQyNiOHAgcFdEHAzcDeybdhsH3JjWb0qfSdvvik8wZZkDpJmVRTt/J80JwHclzSK7xzgxlU8EVknl3wVO/CTX5C62mZVFpV81jIh7gHvS+nPAZ1rYZwGwX7nqdAZpZpbDGaSZlUXNfjvBJ+AAaWZl4dl8zMxyeDYfM7Mc9fithg6QZlYW9RceHSDNrEx8D9LMLIcDpJlZDj/mY2aWwxmkmVkOP+ZjZpbDXWwzsxzuYpuZ5XAGaWaWwxmkmVkOD9KYmeWox3exPWGumVkOZ5BmVhbuYpuZ5ajHLrYDpJmVhTNIM7McziDNzHI4gzQzy+EM0swshzNIM7McEU3VbkLZOUCaWVn4XWwzsxyezcfMLIczSDOzHM4gzcxy+DEfM7McfszHzCyHu9hmZjk8SGNmlqMeM0jPKG5mlsMZpJmVhUexzcxy1GMX2wHSzMrCgzRmZjmcQZqZ5fA9SDOzHH6TxswshzNIM7McvgdpZpbDXWwzsxzOIM3MctRjgFTNXpRUow0zq3MRKuWwbj2GlPRvtnHRKyXV1x5qN0DWOUnjI2JCtdthpfHvr3PwbD7VM77aDbBPxL+/TsAB0swshwOkmVkOB8jq8f2rjs2/v07AgzRmZjmcQZqZ5XCArAJJu0l6WtIsSSdWuz1WPEmXSHpD0hPVbotVngNkO5PUFfgNsDswGjhI0ujqtsqWw6XAbtVuhLUPB8j29xlgVkQ8FxGLgEnA2Cq3yYoUEfcC86rdDmsfDpDtbwjwcsHnhlRmZjXGAbL9tfTeqR8lMKtBDpDtrwEYVvB5KPBqldpiZq1wgGx/04BRkkZI6gEcCNxU5TaZWQscINtZRDQCRwO3AU8B10TEjOq2yool6SrgQWA9SQ2Sjqh2m6xy/CaNmVkOZ5BmZjkcIM3McjhAmpnlcIA0M8vhAGlmlsMBshOS9N/0c7Ck69rY9zhJvZfz/NtLuvmTtLEW67LOxwGyTqRZgpZLRLwaEfu2sdtxwHJGpDmZAAADnElEQVQFSLN64QBZ4yQNlzRT0h8lPSbpuuaMTtILkk6VdD+wn6SRkm6V9Iik+yStn/YbIelBSdMknbHMuZ9I610lnSPp8VTPtyUdAwwG7pZ0d9pvl3SuRyVdK2nFVL5bauf9wD4513KfpE0KPj8gaaNl9pkqaYOCz/dI2lzSZyT9U9K/0s/1Wjj/jyUdX/D5CUnD0/ohkh6SNF3S79P1dpV0adrvcUnfWb7fjtU7B8iOYT1gQkRsBLwDHFmwbUFEbBsRk8i+J+XbEbE5cDzwf2mf84HfRsSWwOs5dYwHRgCbpnqujIgLyN4T3yEidpC0KvBDYOeI2Ax4GPiupF7ARcDewHbAGjl1XAwcBiBpXaBnRDy2zD6TgP3TPoOAwRHxCDAT+FxEbAqcCvw0/z/XR0n6FHAAsE1EbAIsAQ4GNgGGRMSGEfFp4A/FntM6BwfIjuHliHggrV8BbFuw7WqAlMmNAa6VNB34PTAo7bMNcFVavzynjp2B36VXIYmIluY83Ipskt8HUh3jgLWA9YHnI+KZyF7NuiKnjmuBvSR1B75GNvnssq4B9kvr+6djAPqla3sC+CWwQQvH5tkJ2ByYltq9E7A28BywtqRfS9qN7I+P2VLdqt0AK8qy74MWfn4v/ewCvJUypGLOsSwVuc8dEXHQRwqzbnOb76xGxPuS7iCbIHh/YIsW9nlF0pup630A8I206Qzg7oj4Uuo239NCFY189I9+r4J2/zEiTvrYBUkbA7sCR6U2fa2t67DOwxlkx7CmpK3T+kHA/cvuEBHvAM9L2g9AmY3T5gfIZg2CrGvZktuBb0rqlo4fkMrfBVZK61OAbSStk/bpnbrKM4ERkkYWtDHPxcAFwLScLBWybvb3gX4R8Xgq6we8ktYPyznuBWCz1LbNyG4ZANwJ7CtpteZrk7RWumXQJSKuB05pPtasmQNkx/AUME7SY8AA4Lc5+x0MHCHp38AMPvwqh2OBoyRNIws0LbkYeAl4LB3/lVQ+AbhF0t0RMYcsOF2V2jIFWD8iFpDdw/xbGqR5Me9C0v3Ed2j9ft91ZAH9moKys4GfSXoAyBuxvx4YkLrR3wL+k+p8kuze6e2p3XeQ3X4YAtyT9r8U+FiGaZ2bZ/Opcak7eXNEbFjlppSFpMFk3eP1I6Kpys0xa5UzSGs3kg4FpgInOzhaR+AM0swshzNIM7McDpBmZjkcIM3McjhAmpnlcIA0M8vhAGlmluP/AygAtViSkzCYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_svm_train = confusion_matrix(y2_train,svclassifier.predict(x2_train))\n",
    "f, ax = plt.subplots(figsize =(5,5))\n",
    "sns.heatmap(cm_svm_train,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
    "plt.xlabel(\"predicted y values\")\n",
    "plt.ylabel(\"real y values\")\n",
    "plt.title(\"Test for Train Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using K fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9859713257870985"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[x2.columns] = scalerX.fit_transform(x2[x2.columns])\n",
    "svclassifier = SVC(kernel='linear')  \n",
    "cross_val_score(svclassifier, x2, y2, scoring='accuracy', cv = 5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decsion Tree for Admission Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  1.0\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(criterion = \"entropy\")\n",
    "dtc.fit(x2_train,y2_train)\n",
    "print(\"score: \", accuracy_score(y2_test,dtc.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  1.0\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(criterion = \"gini\")\n",
    "dtc.fit(x2_train,y2_train)\n",
    "print(\"score: \", accuracy_score(y2_test,dtc.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score:  0.9000640614990391\n",
      "score:  0.9762972453555413\n",
      "score:  0.7655349135169763\n",
      "score:  0.9410634208840487\n",
      "score:  0.9929532351057014\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  0.9987187700192185\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n",
      "score:  1.0\n"
     ]
    }
   ],
   "source": [
    "# Experimenting with pruning\n",
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= 5)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"gini\",splitter = 'random', max_depth = i)\n",
    "    dtc.fit(x2_train,y2_train)\n",
    "    print(\"score: \", dtc.score(x2_test,y2_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= 5)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"entropy\",splitter = 'random', max_depth = i)\n",
    "    dtc.fit(x2_train,y2_train)\n",
    "    print(\"score: \", dtc.score(x2_test,y2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_dtc = confusion_matrix(y2_test,dtc.predict(x2_test))\n",
    "f, ax = plt.subplots(figsize =(5,5))\n",
    "sns.heatmap(cm_dtc,annot = True,linewidths=0.5,linecolor=\"red\",fmt = \".0f\",ax=ax)\n",
    "plt.title(\"Test for Test Dataset\")\n",
    "plt.xlabel(\"predicted y values\")\n",
    "plt.ylabel(\"real y values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score:  1.0\n",
      "precision_score:  1.0\n",
      "recall_score:  1.0\n",
      "f1_score:  1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy_score: \", accuracy_score(y2_test,dtc.predict(x2_test)))\n",
    "print(\"precision_score: \", precision_score(y2_test,dtc.predict(x2_test)))\n",
    "print(\"recall_score: \", recall_score(y2_test,dtc.predict(x2_test)))\n",
    "print(\"f1_score: \",f1_score(y2_test,dtc.predict(x2_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting for Admission Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AdaBoost = AdaBoostClassifier(base_estimator= dtc,learning_rate=1)\n",
    "AdaBoost.fit(x2_train,y2_train)\n",
    "prediction = AdaBoost.score(x2_test,y2_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score:  0.9737347853939782\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  0.9967969250480462\n",
      "accuracy score:  0.9993593850096092\n",
      "accuracy score:  1.0\n",
      "accuracy score:  0.9967969250480462\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  0.9980781550288277\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n",
      "accuracy score:  1.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,20):\n",
    "    \n",
    "    #dtc = DecisionTreeClassifier(criterion = \"entropy\", splitter = 'random', max_leaf_nodes = 10, min_samples_leaf = 5, max_depth= i)\n",
    "    dtc = DecisionTreeClassifier(criterion = \"gini\",splitter = 'random', max_depth = i)\n",
    "    AdaBoost = AdaBoostClassifier(base_estimator= dtc,learning_rate=1)\n",
    "    AdaBoost.fit(x2_train,y2_train)\n",
    "    prediction = AdaBoost.score(x2_test,y2_test)\n",
    "    print(\"accuracy score: \", accuracy_score(y2_test,AdaBoost.predict(x2_test)))"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}