StudentsAD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "mine=pd.read_csv('/home/ishaan/Desktop/dm.csv')\n",
    "#mine=sns.load_dataset(mine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>id</th>\n",
       "      <th>StudentName</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Grades</th>\n",
       "      <th>Extra CC</th>\n",
       "      <th>College</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ishaan</td>\n",
       "      <td>Maths</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Football</td>\n",
       "      <td>IITD</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Rajat</td>\n",
       "      <td>Mech</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Biking</td>\n",
       "      <td>IITD</td>\n",
       "      <td>5079.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Vaibhav</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>IITB</td>\n",
       "      <td>5600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Rohan</td>\n",
       "      <td>Physics</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>IPU</td>\n",
       "      <td>5871.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Shreya</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>DU</td>\n",
       "      <td>5000.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id StudentName    Subject  Grades  Extra CC College  Ratings\n",
       "0   0     Ishaan       Maths     8.0  Football    IITD   5679.4\n",
       "1   1       Rajat       Mech     7.0    Biking    IITD   5079.4\n",
       "2   2     Vaibhav      Hindi     6.0  Swimming    IITB   5600.0\n",
       "3   3       Rohan    Physics     6.0   Cycling     IPU   5871.2\n",
       "4   4      Shreya  Chemistry     6.0  Swimming      DU   5000.4"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.mean of     id StudentName    Subject  Grades  Extra CC College  Ratings\n",
       "0    0     Ishaan       Maths     8.0  Football    IITD   5679.4\n",
       "1    1       Rajat       Mech     7.0    Biking    IITD   5079.4\n",
       "2    2     Vaibhav      Hindi     6.0  Swimming    IITB   5600.0\n",
       "3    3       Rohan    Physics     6.0   Cycling     IPU   5871.2\n",
       "4    4      Shreya  Chemistry     6.0  Swimming      DU   5000.4\n",
       "5    5     Srishti    Physics     7.5  Swimming     VIT   5679.4\n",
       "6    6      Ishita       Arts     9.0   Running     VIT   5679.4\n",
       "7    7       Heena        Eco    10.0   Drawing     VIT   5679.4\n",
       "8    8     Hassain    Physics     9.0   Drawing   IIITA   5679.4\n",
       "9    9      Chirag    Physics     6.0   Reading    YMCA   5679.4\n",
       "10  10         Anu      Hindi     9.0   Cooking  THAPAR   5679.4\n",
       "11  11        Aman        Eco     9.0   Cooking     PUC   5679.4\n",
       "12  12       Sahil       Arts    10.0   Reading    NSUT   5679.4\n",
       "13  13     Harshit       Arts    10.0   Reading    YMCA   5679.4\n",
       "14  14       Harsh      Hindi     8.0   Singing    IITB   5679.4\n",
       "15  15       Raman       Mech     8.0   Dancing     DTU   5679.4\n",
       "16  16        Raju       Mech     8.0   Dancing     DTU   5679.4\n",
       "17  17      Shilpa  Computers     7.0   Reading    IITD   5679.4\n",
       "18  18       Swati      Hindi     8.0   Singing    YMCA   5679.4\n",
       "19  19    Yogantar      Maths     7.0   Singing    YMCA   5679.4\n",
       "20  20   Parikshit      Maths     7.0   Reading   IIITA   5679.4>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mine.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.mode of     id StudentName    Subject  Grades  Extra CC College  Ratings\n",
       "0    0     Ishaan       Maths     8.0  Football    IITD   5679.4\n",
       "1    1       Rajat       Mech     7.0    Biking    IITD   5079.4\n",
       "2    2     Vaibhav      Hindi     6.0  Swimming    IITB   5600.0\n",
       "3    3       Rohan    Physics     6.0   Cycling     IPU   5871.2\n",
       "4    4      Shreya  Chemistry     6.0  Swimming      DU   5000.4\n",
       "5    5     Srishti    Physics     7.5  Swimming     VIT   5679.4\n",
       "6    6      Ishita       Arts     9.0   Running     VIT   5679.4\n",
       "7    7       Heena        Eco    10.0   Drawing     VIT   5679.4\n",
       "8    8     Hassain    Physics     9.0   Drawing   IIITA   5679.4\n",
       "9    9      Chirag    Physics     6.0   Reading    YMCA   5679.4\n",
       "10  10         Anu      Hindi     9.0   Cooking  THAPAR   5679.4\n",
       "11  11        Aman        Eco     9.0   Cooking     PUC   5679.4\n",
       "12  12       Sahil       Arts    10.0   Reading    NSUT   5679.4\n",
       "13  13     Harshit       Arts    10.0   Reading    YMCA   5679.4\n",
       "14  14       Harsh      Hindi     8.0   Singing    IITB   5679.4\n",
       "15  15       Raman       Mech     8.0   Dancing     DTU   5679.4\n",
       "16  16        Raju       Mech     8.0   Dancing     DTU   5679.4\n",
       "17  17      Shilpa  Computers     7.0   Reading    IITD   5679.4\n",
       "18  18       Swati      Hindi     8.0   Singing    YMCA   5679.4\n",
       "19  19    Yogantar      Maths     7.0   Singing    YMCA   5679.4\n",
       "20  20   Parikshit      Maths     7.0   Reading   IIITA   5679.4>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mine.mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>id</th>\n",
       "      <th>StudentName</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Grades</th>\n",
       "      <th>Extra CC</th>\n",
       "      <th>College</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Ishaan</td>\n",
       "      <td>Maths</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Football</td>\n",
       "      <td>IITD</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Rajat</td>\n",
       "      <td>Mech</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Biking</td>\n",
       "      <td>IITD</td>\n",
       "      <td>5079.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Vaibhav</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>IITB</td>\n",
       "      <td>5600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Rohan</td>\n",
       "      <td>Physics</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Cycling</td>\n",
       "      <td>IPU</td>\n",
       "      <td>5871.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Shreya</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>DU</td>\n",
       "      <td>5000.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Srishti</td>\n",
       "      <td>Physics</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>VIT</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Ishita</td>\n",
       "      <td>Arts</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Running</td>\n",
       "      <td>VIT</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Heena</td>\n",
       "      <td>Eco</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Drawing</td>\n",
       "      <td>VIT</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Hassain</td>\n",
       "      <td>Physics</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Drawing</td>\n",
       "      <td>IIITA</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Chirag</td>\n",
       "      <td>Physics</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Reading</td>\n",
       "      <td>YMCA</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Anu</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>THAPAR</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Aman</td>\n",
       "      <td>Eco</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>PUC</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Sahil</td>\n",
       "      <td>Arts</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Reading</td>\n",
       "      <td>NSUT</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Harshit</td>\n",
       "      <td>Arts</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Reading</td>\n",
       "      <td>YMCA</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Harsh</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Singing</td>\n",
       "      <td>IITB</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Raman</td>\n",
       "      <td>Mech</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>DTU</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Raju</td>\n",
       "      <td>Mech</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>DTU</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Shilpa</td>\n",
       "      <td>Computers</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Reading</td>\n",
       "      <td>IITD</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Swati</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Singing</td>\n",
       "      <td>YMCA</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Yogantar</td>\n",
       "      <td>Maths</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Singing</td>\n",
       "      <td>YMCA</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Parikshit</td>\n",
       "      <td>Maths</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Reading</td>\n",
       "      <td>IIITA</td>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id StudentName    Subject  Grades  Extra CC College  Ratings\n",
       "0    0     Ishaan       Maths     8.0  Football    IITD   5679.4\n",
       "1    1       Rajat       Mech     7.0    Biking    IITD   5079.4\n",
       "2    2     Vaibhav      Hindi     6.0  Swimming    IITB   5600.0\n",
       "3    3       Rohan    Physics     6.0   Cycling     IPU   5871.2\n",
       "4    4      Shreya  Chemistry     6.0  Swimming      DU   5000.4\n",
       "5    5     Srishti    Physics     7.5  Swimming     VIT   5679.4\n",
       "6    6      Ishita       Arts     9.0   Running     VIT   5679.4\n",
       "7    7       Heena        Eco    10.0   Drawing     VIT   5679.4\n",
       "8    8     Hassain    Physics     9.0   Drawing   IIITA   5679.4\n",
       "9    9      Chirag    Physics     6.0   Reading    YMCA   5679.4\n",
       "10  10         Anu      Hindi     9.0   Cooking  THAPAR   5679.4\n",
       "11  11        Aman        Eco     9.0   Cooking     PUC   5679.4\n",
       "12  12       Sahil       Arts    10.0   Reading    NSUT   5679.4\n",
       "13  13     Harshit       Arts    10.0   Reading    YMCA   5679.4\n",
       "14  14       Harsh      Hindi     8.0   Singing    IITB   5679.4\n",
       "15  15       Raman       Mech     8.0   Dancing     DTU   5679.4\n",
       "16  16        Raju       Mech     8.0   Dancing     DTU   5679.4\n",
       "17  17      Shilpa  Computers     7.0   Reading    IITD   5679.4\n",
       "18  18       Swati      Hindi     8.0   Singing    YMCA   5679.4\n",
       "19  19    Yogantar      Maths     7.0   Singing    YMCA   5679.4\n",
       "20  20   Parikshit      Maths     7.0   Reading   IIITA   5679.4"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Grades</th>\n",
       "      <th>Ratings</th>\n",
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       "  <tbody>\n",
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       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171291</td>\n",
       "      <td>0.353365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grades</th>\n",
       "      <td>0.171291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratings</th>\n",
       "      <td>0.353365</td>\n",
       "      <td>0.297406</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "               id    Grades   Ratings\n",
       "id       1.000000  0.171291  0.353365\n",
       "Grades   0.171291  1.000000  0.297406\n",
       "Ratings  0.353365  0.297406  1.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mine.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b61eefa50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mine.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b61a35e50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v391qnxFDWv7Mjx9bz/pN+Z4Os0cVyKVeOhIRcyJiQkSMi4jvJGXfiojZyXpExJcj4riIOCEibi059qaIODpZfpom9rRtyDMo/ieYHxELJY0FlqY8NhPyBbj+hmVc/w8nkMuJu+5dx8rXtvPZT47hxaVbmb+gkWPHD+K6rx/PoIH1nP7+4Xz2k2P4qysbADhkZD9GjujH089trvB3Ut0m/uKfGX7WqfQ9eCjnrHyIpTN+zKqf3lHpsKpWIeC2+3Zw5cUHksvBY4ubWNtY4MOnH8Br6/awePkezjq5H8ceWU++ANt3FvjFH7d3fOIqFlX8vJsiyvv2e8qFD/n9fZldc/f0jney/TLnu09UOoReYeZXhux3j9zLy19LnXMmjDsiU89Zp+3UmyDpPknPJdsnSvpGeUMzM+u87mpDroS0dfsbKT4euBsgGfJ2abmCMjPrqmpOyGnbkAdExAK1fkZ8TxniMTPbL1lMtGmlTchvShpHcZIMJH0CWFu2qMzMuqgQ1duplzYhX0nx8cJjJa0BVgJ/WbaozMy6qOZryMlsR+dKOhDIRcTW8oZlZtY1NZuQJX25jXIAIqLXPT5tZtkWUaMJGRiUfD0GeD8tz3FfCCwoV1BmZl1VqNUackT8A4CkecCk5qYKSdcCd5U9OjOzTuoNnXrvAUpmVaUpKTMzy5SabUMu8XNggaTfJtsfBX5WnpDMzLqultuQAYiI70i6G5iSFH0mIp4qX1hmZl3TG2rIRMQiSauAAwAkHRERr5UtMjOzLqjmGnLayYU+ImkpxQdCHkq+/rGcgZmZdUWhE0vWpO2O/DbwJ8DLEXEUxU8LebxsUZmZdVEhcqmXrEkb0e6IaARyknIR8QAwuYxxmZl1SYRSL1mTtg15s6SBwDzgl5LWA2+XLywzs66p5k69tDXki4DtwJeAu4HlFJ/WMzPLlEKkX7KmwxqypDrgDxFxNsV2cI8/NrPMquYacocJOSLykgqSDoqIt3oiKDOzrspi23BaaduQtwGLJc2lpO04Iq4uS1RmZl2U7wUJ+c5kgeRTQ6CK3xeYWc2q2RqypIuA0RExM9leAIygmJT/rvzhmZl1TmSwsy6tjkZZfJWWOZAB+gKnAFOBz5cpJjOzLqvlT53uGxGrSrYfiYiNwMbk45zMzDIli8PZ0uooIQ8t3YiIq0o2R3R/OGZm+6dQyF7NN62OmiyekHTF3oWS/jv+CCczy6ACSr1kTUc15C8Bv5N0OfBkUnYK0I/iJPVmZplSs516EbE+Ij5Acba3V5JlRkT8aUS8Uf7wzMw6pzsnF5I0TdJLkpZJ+lo7+10sKSRNTrbHSNoh6elkuSFN7Gk/MeR+4P40+5qZVVJ3deol00bMBM4DVgMLJc2OiCV77TcI+CLwxF6nWB4REztzzexNCGpmth8i0i8dOBVYFhErIqIJuJXiRGt7+zbwPWDn/sbuhGxmNSUfSr10YBRQOux3dVL2DkmTgMMj4q59HH+UpKckPSTpjDSxp/5Mva664PLTy32JXm/OWXu/U7Lu9qFrTqt0CL3DV17a71N0plNP0nRgeknRrIiYlfLYHHA98Ol9vLwWOCIiGiWdQnFwxPERsaW9c5Y9IZuZ9aTOJOQk+baVgNcAh5dsj07Kmg0C3gc8KAngEGC2pI9ERAOwK7nGIknLgQlAQ3vxOCGbWU0pdN/kQguB8ZKOopiILwUub34xmY744OZtSQ8CX4mIBkkjgI3J9MVjgfHAio4u6IRsZjWlu8YhR8QeSVcB9wB1wE0R8bykGUBDRMxu5/AzgRmSdlP8YI/PJ9NOtMsJ2cxqSr7QfeeKiDnAnL3KvtXGvlNL1n8D/Kaz13NCNrOaUrPzIZuZVZtqfnTaCdnMakotT79pZlZVXEM2M8sIJ2Qzs4zozlEWPc0J2cxqSsEJ2cwsG9xkYWaWEU7IZmYZ4WFvZmYZEZ2qImfrqT4nZDOrKfl8pSPoOidkM6spbkM2M8sItyGbmWWEa8hmZhkRnaoiu1PPzKxs/Oi0mVlGFKq4EdkJ2cxqituQzcwywgnZzCwjClWckZ2QzaymhDv1zMyyIZ93DdnMLBM6N7lQtjghm1lNqeJRb07IZlZbOvekXrY4IZtZTaniFovelZDHHSoumJRDgqeWF3j0hdY/uUlHi/ePz1EIaNoDdy3I8+YWOOhA+MKH6mjcWtxvzZvBnIYq7srtQceNqecT5/QnJ5i/uIm5C3a1en3KSX05c2I/ImBXU3DL3O2sa/S93R8n3ngdIz80lab1jcw7+cJKh9Pj/KReFZBg2ik5fvlAni074HPn1/HymmLCbfbcK8GTy4qzW08YJc6blONXDxaTw6ZtcOPdVTzzdQVIcMm5/fnx7W+zeWuBr/7lIBYv390q4Ta80MQjzzQBcMK4ei6e2p+Zv3m7UiHXhNU/u5NX/v1mJt70vUqHUhGFKh5lkat0AD3lsGGwaVuw+e3ix4Q//1qBY0a3numpaU/Lep96oHp/rpkw5pA6Nmwq0PhWgXwBFr3YxInj+rTaZ2dTy3rfPvIt7wYbH2lg98a3Kh1GxRQiUi9Z0+kasqShwOER8WwZ4imbwQPElu0t21u2w6jhYu+sO3m8OO2YHHU5uPn+lhrxkIFwxbQ6du2GB57Ns2pDDwVexYYMyrFpa0ttePO2AmMOffev3JkT+3LO5H7U58QPb9vWkyFaDerOYW+SpgE/BOqAn0TEP+31+ueBK4E8sA2YHhFLkteuAT6bvHZ1RNzT0fVSJWRJDwIfSfZfBKyXND8ivpzy+6oaDUuDhqV53nekmPK+HLMfL7BtB/zo93l2NMEhQ+GSM+q4YU6+VY3aum7e003Me7qJycf2YdqfHsAv/ri944PM2tBdbciS6oCZwHnAamChpNnNCTdxS0TckOz/EeB6YJqk44BLgeOBw4B7JU2IiHbbPdM2WRwUEVuAjwM/j4jTgHPb+UamS2qQ1NBw340pL1FeW7YHgwe0bA8eAFt3tP2De+7V4JhRxSaNfAF2JG+t120qticPH1zOaGvD5q0Fhg5q+RUbMjDH5q1td9gtenE3Jx3dp83XzdKISL904FRgWUSsiIgm4FbgotbXipJeKA6k5S33RcCtEbErIlYCy5LztSttQq6XdChwCfCHjnaOiFkRMTkiJk/+4BUpL1Fer2+EYYPEkAMhl4Pjj8jx8urWP5FhA1vWxx8mNiajKgb0K3ZQAQw5EIYNKiZla9+r6/KMHJpj+EHFJqBTju3L4uW7W+0zYkjLr+DxY+tZv8kdp7Z/8vlC6qUDo4BVJdurk7JWJF0paTnwfeDqzhy7t7RtyDOAe4BHImKhpLHA0pTHZkIE3N1Q4PKpdUjwzIoCG7bAWSfkWLsxeHlNMHlCjrGHiHwBdjYFsx8vJocjRoipJ+bIF4rnmbOw0KozyvatEHDbfTu48uIDyeXgscVNrG0s8OHTD+C1dXtYvHwPZ53cj2OPrCdfgO07C26u6AYTf/HPDD/rVPoePJRzVj7E0hk/ZtVP76h0WD2mMw+GSJoOTC8pmhURszp1vYiZwExJlwPfAD7VmeNLpUrIEXE7cHvJ9grg4q5etFKWrQ2W3dW6BvbQ4pb/kv/vyX3/x3xxdfDiatfcuuL5lXt4fuXWVmV3zd/5zvodD+zo6ZBq3tN/9TeVDqGiOpOQk+TbVgJeAxxesj06KWvLrcB/dPFYIH2n3o/2UfwW0BARv09zDjOzntCNz4UsBMZLOopiMr0UuLx0B0njI6K5teDDtLQczAZukXQ9xU698cCCji6YtsniAOBYWmrJFwMrgZMknR0R/zPleczMyqq75rKIiD2SrqLYXFsH3BQRz0uaQbEyOhu4StK5wG5gE0lzRbLfbcASYA9wZUcjLCB9Qj4ROL35hJL+A3gYmAIs7sw3aWZWTt05Djki5gBz9ir7Vsn6F9s59jvAdzpzvbQJeSgwkGIzBRSHdwyLiLykXW0fZmbWs1KMnsistAn5+8DTyQMiAs4ErpN0IHBvmWIzM+u0mp9+MyL+r6Q5tAxs/npEvJ6s/21ZIjMz64KaT8iJHLAhOeZoSUdHxLzyhGVm1jVZnDQorbTD3r4H/AXwPNDcQBOAE7KZZUpvqCF/FDgmItyBZ2aZ1hs+5HQF0AdwQjazTMvvqf1RFtspjrK4j5KkHBFXt32ImVnP6w015NnJYmaWaVGo8RpyRPys3IGYmXWHmv2QU0m3RcQlkhazj0+Yi4gTyxaZmVkX1HKTRfNz2v+t3IGYmXWHQhV36rX7iSERsTZZ/euIeLV0Af66/OGZmXVOIQqpl6xJ+xFO5+2j7M+6MxAzs+4QhUi9ZE1HbchfoFgTHivp2ZKXBgHzyxmYmVlXZDHRptVRG/ItwB+B7wJfKynfGhEbyxaVmVkX1WynXkS8RXEO5MsAJI2k+OkhAyUNjIjXyh+imVl6hVofhyzpQqD5s6HWA0cCLwDHly80M7POK+Sr9wOJ03bq/SPwJ8DLEXEU8EHg8bJFZWbWRdXcqZc2Ie+OiEYgJykXEQ8Ak8sYl5lZl1RzQk47l8VmSQMpzn/8S0nrgbfLF5aZWddkcXxxWmlryBdRnPHtS8DdwHLgwnIFZWbWVTVfQ46I5tpwAfiZpBzFkRe/LFdgZmZdUc2zvbVbQ5Y0WNI1kv5N0vkquorihPWX9EyIZmbpFfL51EvWdFRD/gWwCXgM+BzwdUDARyPi6TLHZmbWaTU7/SYwNiJOAJD0E2AtcERE7Cx7ZGZmXVDNTRYdJeTdzSsRkZe02snYzLIsi511aXWUkE+StCVZF9A/2RYQETG4rNGZmXVSVPGwt47msqjrqUDMzLpDYU/2OuvSUjXPjFQukqZHxKxKx1HLfI/Lz/e4+qR9MKS3mV7pAHoB3+Py8z2uMk7IZmYZ4YRsZpYRTsj75na38vM9Lj/f4yrjTj0zs4xwDdnMLCOckAFJj7ZR/p+SPtHT8VQDSe+RdIukFZIWSXpM0sf243zXSvpKd8ZYzSTlJT0t6TlJ/yVpSAf7D5H01yXbh0m6o/yRWndyQgYi4gOVjqGaSBLwO2BeRIyNiFOAS4HRe+2X9gMQ7N12RMTEiHgfsBG4soP9hwDvJOSIeD0iXJmoMk7IgKRtyVclU42+JOleYGSFQ8uqc4CmiLihuSAiXo2IH0v6tKTZku4H7pM0UNJ9kp6UtFjSRc3HSPpfkl6W9AhwTEn5OEl3JzXvhyUdm5T/eVJjfEbSvB78fivtMWAUQDv385+AcUmt+geSxkh6Ljnm05LuTO7pUknfbz6xpM8mP4MFkm6U9G9JeW+915UVEb1+AbYlXz8OzAXqKH7C9mbgE5WOL2sLcDXwL2289mlgNTAs2a4HBifrBwPLKM6FcgqwGBgADE7Kv5Lsdx8wPlk/Dbg/WV8MjErWh1T6PpT5Hjf/TtYBtwPTOrifY4DnSo5/Zzv5mawADgIOAF4FDk9+x18BhgF9gIeBf+tt9zpLi99StnYm8KuIyAOvJ7U864CkmcAUoAmYCcyNiI3NLwPXSTqT4ifOjALeA5wB/DYitifnmJ18HQh8ALi92DICQL/k63zgPyXdBtxZ7u+rwvpLepri/XqBYkUB2r6fHbkvIt4CkLQEOJJiQn+o+Wcl6XZgQrJ/b7rXmeEmC+uK54FJzRsRcSXwQWBEUlT6AbifTMpPiYiJwBsUa2ltyQGbo9h+2ry8N7nO54FvUKzdLZI0vLu+oQzakdyvIykm4eY25M7ez2a7StbzdDyxWG+615nhhNzaPOAvJNVJOhQ4u9IBZdT9wAGSvlBSNqCNfQ8C1kfEbklnU0wwULzXH5XUX9Igkg/NjYgtwEpJfw7vtOuflKyPi4gnIuJbwAaKyaKmJe8grgb+Jukkbet+bgUGdfL0C4GzJA1Nzn1x8wu98V5ngZssWvstxQ6rJcBrFDtTbC8REZI+CvyLpK9S/IN9G/g7oP9eu/8S+C9Ji4EG4MXkHE9K+jXwDLCeYnJo9kngPyR9g2Lb5q3Jfj+QNJ5ijfG+pKzmRcRTkp6l5YOF93U/GyXNTzry/kix6aij866RdB2wgOJIjheBt5KXe+W9rjQ/qWfWi0kaGBHbkhryb4GbIuK3lY6rt3KThVnvdm3SefgcsJLi+HIFHZmbAAAAL0lEQVSrENeQzcwywjVkM7OMcEI2M8sIJ2Qzs4xwQjYzywgnZDOzjHBCNjPLiP8PZUTsHNki2cUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mine.corr(),cmap='coolwarm',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b65962a90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mine.corr(),cmap='coolwarm',annot=True,linecolor='white',linewidths=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f1b61bb2bd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(mine.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f1b61ebc810>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(mine.corr(),cmap='rainbow',annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mine = datasets.load_mine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Grades\n",
       "0      8.0\n",
       "1      7.0\n",
       "2      6.0\n",
       "3      6.0\n",
       "4      6.0\n",
       "5      7.5\n",
       "6      9.0\n",
       "7     10.0\n",
       "8      9.0\n",
       "9      6.0\n",
       "10     9.0\n",
       "11     9.0\n",
       "12    10.0\n",
       "13    10.0\n",
       "14     8.0\n",
       "15     8.0\n",
       "16     8.0\n",
       "17     7.0\n",
       "18     8.0\n",
       "19     7.0\n",
       "20     7.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = pd.DataFrame(mine['Ratings'])\n",
    "y1 = pd.DataFrame(mine['Grades'])\n",
    "#X1 = pd.DataFrame(mine.data[:,2:3], columns=mine.feature_names[2:3])\n",
    "#y1 = pd.DataFrame(mine.data[:,3:4], columns=mine.feature_names[3:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5679.4</td>\n",
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       "      <th>2</th>\n",
       "      <td>5600.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5871.2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000.4</td>\n",
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       "      <td>5679.4</td>\n",
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       "      <td>5679.4</td>\n",
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       "      <th>8</th>\n",
       "      <td>5679.4</td>\n",
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       "      <th>9</th>\n",
       "      <td>5679.4</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5679.4</td>\n",
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       "      <th>11</th>\n",
       "      <td>5679.4</td>\n",
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       "      <th>12</th>\n",
       "      <td>5679.4</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ratings\n",
       "0    5679.4\n",
       "1    5079.4\n",
       "2    5600.0\n",
       "3    5871.2\n",
       "4    5000.4\n",
       "5    5679.4\n",
       "6    5679.4\n",
       "7    5679.4\n",
       "8    5679.4\n",
       "9    5679.4\n",
       "10   5679.4\n",
       "11   5679.4\n",
       "12   5679.4\n",
       "13   5679.4\n",
       "14   5679.4\n",
       "15   5679.4\n",
       "16   5679.4\n",
       "17   5679.4\n",
       "18   5679.4\n",
       "19   5679.4\n",
       "20   5679.4"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Grades\n",
       "0      8.0\n",
       "1      7.0\n",
       "2      6.0\n",
       "3      6.0\n",
       "4      6.0\n",
       "5      7.5\n",
       "6      9.0\n",
       "7     10.0\n",
       "8      9.0\n",
       "9      6.0\n",
       "10     9.0\n",
       "11     9.0\n",
       "12    10.0\n",
       "13    10.0\n",
       "14     8.0\n",
       "15     8.0\n",
       "16     8.0\n",
       "17     7.0\n",
       "18     8.0\n",
       "19     7.0\n",
       "20     7.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(X1, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00199458]]\n"
     ]
    }
   ],
   "source": [
    "print(regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.33626868]\n"
     ]
    }
   ],
   "source": [
    "print(regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(mine['Ratings'])\n",
    "y = pd.DataFrame(mine['Grades'])\n",
    "#X = pd.DataFrame(mine.data[:,0:3], columns=mine.feature_names[0:3])\n",
    "#y = pd.DataFrame(mine.data[:,3:4], columns=mine.feature_names[3:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5679.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5079.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5871.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ratings\n",
       "0   5679.4\n",
       "1   5079.4\n",
       "2   5600.0\n",
       "3   5871.2\n",
       "4   5000.4"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Grades</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Grades\n",
       "0     8.0\n",
       "1     7.0\n",
       "2     6.0\n",
       "3     6.0\n",
       "4     6.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "#data = load_mine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#data1 = pd.DataFrame(data= np.c_[mine['data'], mine['target']],columns= mine['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1b610146d0>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Grades',y='Ratings',data=mine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b61058190>"
      ]
     },
     "execution_count": 80,
     "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.distplot(mine['Ratings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b60e9dcd0>"
      ]
     },
     "execution_count": 81,
     "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.distplot(mine['Ratings'],kde=False,bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f1b61853b50>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(mine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b60f46fd0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l4/VF4OIkZ3T7vhQ4MDAzjY/VGJpr4Dr2lsHtKyXJ07vvz6J3XftDAyNTOb+G5Zry7yN8/3l0JfC3i8zsA16R5Jzur7dXdOtO3iSeQT7ZL+CngDngXnp/Sp0DvAF4Q7c99P5HIp8D/hOYXSW5dgL7gXuA24GXTijXmfRO2rP61q2G4zUs17SO1+8Dn6F33fZ64EnAtcCWbvvp9C4jzQP/AfzYKsn1h33H6zbgggnl+ld6D2TuAS5dRefXsFwTO7/oPYB5CPgOvb/yr6b3PM2twIP0Xs3z1G52FnhP38/+SneuzQOvXW4WP35Akhq0ai/LSJJOnuUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGvR/wFoTt8MmnlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.rugplot(mine['Grades'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b60f51a10>"
      ]
     },
     "execution_count": 75,
     "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.kdeplot(mine['Ratings'])\n",
    "sns.rugplot(mine['Ratings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b604a6610>"
      ]
     },
     "execution_count": 85,
     "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.barplot(x='Subject',y='Grades',data=mine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b6027fed0>"
      ]
     },
     "execution_count": 89,
     "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.violinplot(x=\"Grades\", y=\"Ratings\", data=mine,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b5ff04a10>"
      ]
     },
     "execution_count": 93,
     "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.violinplot(x=\"Grades\", y=\"Ratings\", data=mine,palette='rainbow')\n",
    "sns.swarmplot(x=\"Grades\", y=\"Ratings\", data=mine,color='black',size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1b6004a1d0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Grades',y='Ratings',data=mine,hue='Subject',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1b5fc92a90>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Grades',y='Ratings',data=mine,hue='Subject',palette='coolwarm')\n",
    "           #markers=['o','v','o'],scatter_kws={'s':100}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f1b5fc63510>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x360 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Grades',y='Ratings',data=mine,col='Subject')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}