Student_Performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Student's Performance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.read_csv(\"C:\\\\Users\\\\Dell\\\\Udemy\\\\xAPI-Edu-Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender NationalITy PlaceofBirth     StageID GradeID SectionID Topic  \\\n",
       "0      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "1      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "2      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "3      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "4      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "\n",
       "  Semester Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "0        F   Father           15                16                  2   \n",
       "1        F   Father           20                20                  3   \n",
       "2        F   Father           10                 7                  0   \n",
       "3        F   Father           30                25                  5   \n",
       "4        F   Father           40                50                 12   \n",
       "\n",
       "   Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "0          20                   Yes                     Good   \n",
       "1          25                   Yes                     Good   \n",
       "2          30                    No                      Bad   \n",
       "3          35                    No                      Bad   \n",
       "4          50                    No                      Bad   \n",
       "\n",
       "  StudentAbsenceDays Class  \n",
       "0            Under-7     M  \n",
       "1            Under-7     M  \n",
       "2            Above-7     L  \n",
       "3            Above-7     L  \n",
       "4            Above-7     M  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ds.iloc[:,:-1].values\n",
    "y = ds.iloc[:,16].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x22127fbddd8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22128a8d320>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(ds[\"gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22128d3aba8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(context = 'paper')\n",
    "plt.figure(figsize=(14,4))\n",
    "sns.countplot(x = \"NationalITy\",hue = 'gender',data = ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22128de8cc0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot('StageID',data = ds,hue = 'gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22128f5bbe0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot('GradeID',data = ds,hue = 'gender')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22128e8a080>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,3))\n",
    "sns.barplot(x = 'Topic',y = 'raisedhands',data = ds,hue = 'gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  NationalITy  PlaceofBirth  StageID  GradeID  SectionID  Topic  \\\n",
       "0       1            4             4        2        1          0      7   \n",
       "1       1            4             4        2        1          0      7   \n",
       "2       1            4             4        2        1          0      7   \n",
       "3       1            4             4        2        1          0      7   \n",
       "4       1            4             4        2        1          0      7   \n",
       "\n",
       "   Semester  Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "0         0         0           15                16                  2   \n",
       "1         0         0           20                20                  3   \n",
       "2         0         0           10                 7                  0   \n",
       "3         0         0           29                24                  5   \n",
       "4         0         0           35                43                 12   \n",
       "\n",
       "   Discussion  ParentAnsweringSurvey  ParentschoolSatisfaction  \\\n",
       "0          18                      1                         1   \n",
       "1          23                      1                         1   \n",
       "2          28                      0                         0   \n",
       "3          33                      0                         0   \n",
       "4          46                      0                         0   \n",
       "\n",
       "   StudentAbsenceDays  Class  \n",
       "0                   1      2  \n",
       "1                   1      2  \n",
       "2                   0      1  \n",
       "3                   0      1  \n",
       "4                   0      2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "le = LabelEncoder()\n",
    "X_2 = ds.apply(le.fit_transform)\n",
    "X_2.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
      "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
      "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(categorical_features=None, categories=None, drop=None,\n",
       "              dtype=<class 'numpy.float64'>, handle_unknown='error',\n",
       "              n_values=None, sparse=True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = OneHotEncoder()\n",
    "enc.fit(X_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 420)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehotlabels = enc.transform(X_2).toarray()\n",
    "onehotlabels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 1.],\n",
       "       [0., 1., 0., ..., 0., 0., 1.],\n",
       "       [0., 1., 0., ..., 0., 1., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 0., 1., 0.],\n",
       "       [1., 0., 0., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehotlabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  NationalITy  PlaceofBirth  StageID  GradeID  SectionID  Topic  \\\n",
       "0       1            4             4        2        1          0      7   \n",
       "1       1            4             4        2        1          0      7   \n",
       "2       1            4             4        2        1          0      7   \n",
       "3       1            4             4        2        1          0      7   \n",
       "4       1            4             4        2        1          0      7   \n",
       "\n",
       "   Semester  Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "0         0         0           15                16                  2   \n",
       "1         0         0           20                20                  3   \n",
       "2         0         0           10                 7                  0   \n",
       "3         0         0           29                24                  5   \n",
       "4         0         0           35                43                 12   \n",
       "\n",
       "   Discussion  ParentAnsweringSurvey  ParentschoolSatisfaction  \\\n",
       "0          18                      1                         1   \n",
       "1          23                      1                         1   \n",
       "2          28                      0                         0   \n",
       "3          33                      0                         0   \n",
       "4          46                      0                         0   \n",
       "\n",
       "   StudentAbsenceDays  Class  \n",
       "0                   1      2  \n",
       "1                   1      2  \n",
       "2                   0      1  \n",
       "3                   0      1  \n",
       "4                   0      2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 17)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>VisITedResources</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StageID</th>\n",
       "      <th>Semester</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>gender</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Relation</th>\n",
       "      <th>Topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VisITedResources  NationalITy  PlaceofBirth  raisedhands  \\\n",
       "0                16            4             4           15   \n",
       "1                20            4             4           20   \n",
       "2                 7            4             4           10   \n",
       "3                24            4             4           29   \n",
       "4                43            4             4           35   \n",
       "\n",
       "   ParentAnsweringSurvey  ParentschoolSatisfaction  StageID  Semester  \\\n",
       "0                      1                         1        2         0   \n",
       "1                      1                         1        2         0   \n",
       "2                      0                         0        2         0   \n",
       "3                      0                         0        2         0   \n",
       "4                      0                         0        2         0   \n",
       "\n",
       "   StudentAbsenceDays  SectionID  gender  GradeID  Discussion  \\\n",
       "0                   1          0       1        1          18   \n",
       "1                   1          0       1        1          23   \n",
       "2                   0          0       1        1          28   \n",
       "3                   0          0       1        1          33   \n",
       "4                   0          0       1        1          46   \n",
       "\n",
       "   AnnouncementsView  Relation  Topic  \n",
       "0                  2         0      7  \n",
       "1                  3         0      7  \n",
       "2                  0         0      7  \n",
       "3                  5         0      7  \n",
       "4                 12         0      7  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X_2[{'gender','NationalITy','PlaceofBirth','StageID','GradeID','SectionID','Topic','Semester','Relation','raisedhands','VisITedResources','AnnouncementsView','Discussion','ParentAnsweringSurvey','ParentschoolSatisfaction','StudentAbsenceDays'}]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    1\n",
       "3    1\n",
       "4    2\n",
       "Name: Class, dtype: int32"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = X_2['Class']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Subset Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_2, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.215e+01 6.955e+00 9.757e+00 1.361e+00 7.187e+00 5.159e-01 1.390e+01\n",
      " 3.992e+00 4.797e+01 2.958e+03 3.907e+03 2.415e+03 7.111e+02 4.172e+01\n",
      " 2.682e+01 8.961e+01 3.000e+02]\n",
      " \n"
     ]
    }
   ],
   "source": [
    "testf = SelectKBest(score_func = chi2, k = 4)\n",
    "fit = testf.fit(X_2,y)\n",
    "np.set_printoptions(precision=3)\n",
    "print(fit.scores_)\n",
    "print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = fit.transform(X_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15 16  2 18]\n",
      " [20 20  3 23]\n",
      " [10  7  0 28]\n",
      " [29 24  5 33]\n",
      " [35 43 12 46]]\n"
     ]
    }
   ],
   "source": [
    "print(features[0:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "      normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha = 1.0)\n",
    "ridge.fit(X_2,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_feat(coefs, names = None, sort = False):\n",
    "    if names == None:\n",
    "        names = [\"X%s\" % x for x in range(len(coefs))]\n",
    "    lst = zip(coefs, names)\n",
    "    if sort:\n",
    "        lst = sorted(lst,key = lambda x:-np.abs(x[0]))\n",
    "    return\"+\".join(\"%s * %s\"%(round(coef,3), name) for coef,name in lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Model:  0.0 * X0+0.0 * X1+-0.0 * X2+-0.0 * X3+0.0 * X4+0.0 * X5+0.0 * X6+-0.0 * X7+-0.001 * X8+-0.0 * X9+0.0 * X10+0.0 * X11+-0.0 * X12+-0.0 * X13+-0.0 * X14+-0.001 * X15+0.997 * X16\n"
     ]
    }
   ],
   "source": [
    "print(\"Ridge Model: \", print_feat(ridge.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Modelling Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import confusion_matrix\n",
    "#cm = confusion_matrix(y_test,y_pred)\n",
    "#cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig,axis = plt.subplots(ncols = 2)\n",
    "#plt.figure(num = 2,figsize=(15,8))\n",
    "#sns.countplot(y_test,ax = axis[0])\n",
    "#sns.countplot(y_pred,ax = axis[1])\n",
    "\n",
    "#sns.countplot(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_score = np.trace(cm)/np.sum(cm)\n",
    "#c_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K Neighbors Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.neighbors import KNeighborsClassifier\n",
    "#K_classifier = KNeighborsClassifier(n_neighbors = 3,metric = 'minkowski',p = 2)\n",
    "#K_classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#knn_y_pred = K_classifier.predict(X_test)\n",
    "#from sklearn.metrics import confusion_matrix\n",
    "#K_cm = confusion_matrix(y_test,knn_y_pred)\n",
    "#K_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#knn_score = np.trace(K_cm)/np.sum(K_cm)\n",
    "#knn_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='poly', max_iter=-1, probability=False, random_state=0,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC(kernel = 'poly' ,random_state=0)\n",
    "svc.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_y_pred = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33,  3,  0],\n",
       "       [ 5, 40,  1],\n",
       "       [ 1, 10, 66]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "svc_cm = confusion_matrix(y_test,svc_y_pred)\n",
    "svc_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8742138364779874"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_score = np.trace(svc_cm)/np.sum(svc_cm)\n",
    "svc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb_classifier = GaussianNB()\n",
    "nb_classifier.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_y_pred = nb_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36,  0,  0],\n",
       "       [ 0, 46,  0],\n",
       "       [ 0,  0, 77]], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "nb_cm = confusion_matrix(y_test,nb_y_pred)\n",
    "nb_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_score = np.trace(nb_cm)/np.sum(nb_cm)\n",
    "nb_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree_classifier = DecisionTreeClassifier(criterion='entropy',random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_classifier.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 0, 0, 1, 2, 1, 2, 2, 2, 0, 2, 2, 0, 0, 1, 2, 1, 2, 0, 0,\n",
       "       1, 2, 2, 1, 0, 2, 1, 2, 2, 2, 1, 0, 2, 0, 2, 1, 2, 2, 2, 0, 1, 2,\n",
       "       2, 1, 0, 2, 0, 2, 2, 2, 1, 2, 2, 2, 1, 0, 2, 1, 2, 1, 1, 2, 2, 2,\n",
       "       1, 0, 1, 0, 2, 0, 2, 2, 2, 1, 2, 1, 1, 2, 0, 2, 1, 0, 0, 2, 2, 1,\n",
       "       1, 2, 2, 2, 2, 2, 1, 0, 1, 0, 2, 1, 2, 1, 1, 0, 2, 2, 0, 2, 2, 1,\n",
       "       2, 2, 2, 0, 2, 1, 0, 1, 0, 1, 2, 2, 2, 0, 1, 0, 1, 1, 2, 2, 1, 1,\n",
       "       1, 1, 1, 1, 2, 2, 2, 1, 2, 0, 2, 1, 2, 1, 0, 0, 0, 2, 0, 0, 2, 2,\n",
       "       2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_y_pred = tree_classifier.predict(X_test)\n",
    "tree_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36,  0,  0],\n",
       "       [ 0, 46,  0],\n",
       "       [ 0,  0, 77]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "tree_cm = confusion_matrix(y_test,tree_y_pred)\n",
    "tree_cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_score = np.trace(tree_cm)/np.sum(tree_cm)\n",
    "tree_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rand_classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n",
    "rand_classifier.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_y_pred = rand_classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34,  2,  0],\n",
       "       [ 0, 46,  0],\n",
       "       [ 0,  0, 77]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "rand_cm = confusion_matrix(y_test,rand_y_pred)\n",
    "rand_cm"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}