dtascs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1) Encoding Technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>y</th>\n",
       "      <th>X0</th>\n",
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       "      <th>X375</th>\n",
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       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
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       "      <th>X380</th>\n",
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       "      <th>0</th>\n",
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       "      <td>130.81</td>\n",
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       "      <td>88.53</td>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>av</td>\n",
       "      <td>e</td>\n",
       "      <td>d</td>\n",
       "      <td>y</td>\n",
       "      <td>l</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>76.26</td>\n",
       "      <td>az</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>c</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>80.62</td>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>x</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>78.02</td>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID       y  X0 X1  X2 X3 X4 X5 X6 X8  ...  X375  X376  X377  X378  X379  \\\n",
       "0   0  130.81   k  v  at  a  d  u  j  o  ...     0     0     1     0     0   \n",
       "1   6   88.53   k  t  av  e  d  y  l  o  ...     1     0     0     0     0   \n",
       "2   7   76.26  az  w   n  c  d  x  j  x  ...     0     0     0     0     0   \n",
       "3   9   80.62  az  t   n  f  d  x  l  e  ...     0     0     0     0     0   \n",
       "4  13   78.02  az  v   n  f  d  h  d  n  ...     0     0     0     0     0   \n",
       "\n",
       "   X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0  \n",
       "1     0     0     0     0     0  \n",
       "2     0     1     0     0     0  \n",
       "3     0     0     0     0     0  \n",
       "4     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 378 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Mercedesdata.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 378)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
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       "      <th>1</th>\n",
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       "      <td>av</td>\n",
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       "      <td>l</td>\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <td>v</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  X1  X2 X3 X4 X5 X6\n",
       "0  v  at  a  d  u  j\n",
       "1  t  av  e  d  y  l\n",
       "2  w   n  c  d  x  j\n",
       "3  t   n  f  d  x  l\n",
       "4  v   n  f  d  h  d"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Mercedesdata.csv', usecols=['X1', 'X2', 'X3', 'X4', 'X5', 'X6'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X1 :  27 labels\n",
      "X2 :  44 labels\n",
      "X3 :  7 labels\n",
      "X4 :  4 labels\n",
      "X5 :  29 labels\n",
      "X6 :  12 labels\n"
     ]
    }
   ],
   "source": [
    "for i in data.columns:\n",
    "    print(i, ': ',len(data[i].unique()),'labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>False</td>\n",
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       "      <th>4205</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4209 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      X1_aa  X1_ab   X1_b   X1_c   X1_d   X1_e   X1_f   X1_g   X1_h   X1_i  \\\n",
       "0     False  False  False  False  False  False  False  False  False  False   \n",
       "1     False  False  False  False  False  False  False  False  False  False   \n",
       "2     False  False  False  False  False  False  False  False  False  False   \n",
       "3     False  False  False  False  False  False  False  False  False  False   \n",
       "4     False  False  False  False  False  False  False  False  False  False   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "4204  False  False  False  False  False  False  False  False  False  False   \n",
       "4205  False  False  False  False  False  False  False  False  False  False   \n",
       "4206  False  False  False  False  False  False  False  False  False  False   \n",
       "4207  False  False  False  False  False  False  False  False  False  False   \n",
       "4208  False  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "      ...   X6_c   X6_d   X6_e   X6_f   X6_g   X6_h   X6_i   X6_j   X6_k  \\\n",
       "0     ...  False  False  False  False  False  False  False   True  False   \n",
       "1     ...  False  False  False  False  False  False  False  False  False   \n",
       "2     ...  False  False  False  False  False  False  False   True  False   \n",
       "3     ...  False  False  False  False  False  False  False  False  False   \n",
       "4     ...  False   True  False  False  False  False  False  False  False   \n",
       "...   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "4204  ...  False   True  False  False  False  False  False  False  False   \n",
       "4205  ...  False  False  False  False  False   True  False  False  False   \n",
       "4206  ...  False  False  False  False   True  False  False  False  False   \n",
       "4207  ...  False  False  False  False  False  False  False  False  False   \n",
       "4208  ...  False  False  False  False   True  False  False  False  False   \n",
       "\n",
       "       X6_l  \n",
       "0     False  \n",
       "1      True  \n",
       "2     False  \n",
       "3      True  \n",
       "4     False  \n",
       "...     ...  \n",
       "4204  False  \n",
       "4205  False  \n",
       "4206  False  \n",
       "4207   True  \n",
       "4208  False  \n",
       "\n",
       "[4209 rows x 117 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.get_dummies(data, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X2\n",
       "as    1659\n",
       "ae     496\n",
       "ai     415\n",
       "m      367\n",
       "ak     265\n",
       "r      153\n",
       "n      137\n",
       "s       94\n",
       "f       87\n",
       "e       81\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.X2.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['as', 'ae', 'ai', 'm', 'ak', 'r', 'n', 's', 'f', 'e']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10 = list(data.X2.value_counts().head(10).index)\n",
    "top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>m</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>m</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ai</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>k</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>as</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X2  as  ae  ai  m  ak  r  n  s  f  e\n",
       "0   at   0   0   0  0   0  0  0  0  0  0\n",
       "1   av   0   0   0  0   0  0  0  0  0  0\n",
       "2    n   0   0   0  0   0  0  1  0  0  0\n",
       "3    n   0   0   0  0   0  0  1  0  0  0\n",
       "4    n   0   0   0  0   0  0  1  0  0  0\n",
       "5    e   0   0   0  0   0  0  0  0  0  1\n",
       "6    e   0   0   0  0   0  0  0  0  0  1\n",
       "7   as   1   0   0  0   0  0  0  0  0  0\n",
       "8   as   1   0   0  0   0  0  0  0  0  0\n",
       "9   aq   0   0   0  0   0  0  0  0  0  0\n",
       "10   r   0   0   0  0   0  1  0  0  0  0\n",
       "11   e   0   0   0  0   0  0  0  0  0  1\n",
       "12  ai   0   0   1  0   0  0  0  0  0  0\n",
       "13   e   0   0   0  0   0  0  0  0  0  1\n",
       "14  as   1   0   0  0   0  0  0  0  0  0\n",
       "15  ak   0   0   0  0   1  0  0  0  0  0\n",
       "16   e   0   0   0  0   0  0  0  0  0  1\n",
       "17  as   1   0   0  0   0  0  0  0  0  0\n",
       "18   m   0   0   0  1   0  0  0  0  0  0\n",
       "19  ak   0   0   0  0   1  0  0  0  0  0\n",
       "20   m   0   0   0  1   0  0  0  0  0  0\n",
       "21  as   1   0   0  0   0  0  0  0  0  0\n",
       "22   a   0   0   0  0   0  0  0  0  0  0\n",
       "23   m   0   0   0  1   0  0  0  0  0  0\n",
       "24  ak   0   0   0  0   1  0  0  0  0  0\n",
       "25   m   0   0   0  1   0  0  0  0  0  0\n",
       "26  as   1   0   0  0   0  0  0  0  0  0\n",
       "27  as   1   0   0  0   0  0  0  0  0  0\n",
       "28  as   1   0   0  0   0  0  0  0  0  0\n",
       "29   m   0   0   0  1   0  0  0  0  0  0\n",
       "30  as   1   0   0  0   0  0  0  0  0  0\n",
       "31  as   1   0   0  0   0  0  0  0  0  0\n",
       "32   m   0   0   0  1   0  0  0  0  0  0\n",
       "33  as   1   0   0  0   0  0  0  0  0  0\n",
       "34  as   1   0   0  0   0  0  0  0  0  0\n",
       "35  ai   0   0   1  0   0  0  0  0  0  0\n",
       "36  as   1   0   0  0   0  0  0  0  0  0\n",
       "37   k   0   0   0  0   0  0  0  0  0  0\n",
       "38  as   1   0   0  0   0  0  0  0  0  0\n",
       "39  as   1   0   0  0   0  0  0  0  0  0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for label in top_10:\n",
    "    data[label] = np.where(data['X2']==label,1,0)\n",
    "data[['X2']+top_10].head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4204</th>\n",
       "      <td>s</td>\n",
       "      <td>as</td>\n",
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       "      <td>aa</td>\n",
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       "    <tr>\n",
       "      <th>4205</th>\n",
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       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>r</td>\n",
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       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>r</td>\n",
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       "      <td>d</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4209 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     X1  X2 X3 X4  X5 X6  X2_as  X2_ae  X2_ai  X2_m  X2_ak  X2_r  X2_n  X2_s  \\\n",
       "0     v  at  a  d   u  j      0      0      0     0      0     0     0     0   \n",
       "1     t  av  e  d   y  l      0      0      0     0      0     0     0     0   \n",
       "2     w   n  c  d   x  j      0      0      0     0      0     0     1     0   \n",
       "3     t   n  f  d   x  l      0      0      0     0      0     0     1     0   \n",
       "4     v   n  f  d   h  d      0      0      0     0      0     0     1     0   \n",
       "...  ..  .. .. ..  .. ..    ...    ...    ...   ...    ...   ...   ...   ...   \n",
       "4204  s  as  c  d  aa  d      1      0      0     0      0     0     0     0   \n",
       "4205  o   t  d  d  aa  h      0      0      0     0      0     0     0     0   \n",
       "4206  v   r  a  d  aa  g      0      0      0     0      0     1     0     0   \n",
       "4207  r   e  f  d  aa  l      0      0      0     0      0     0     0     0   \n",
       "4208  r  ae  c  d  aa  g      0      1      0     0      0     0     0     0   \n",
       "\n",
       "      X2_f  X2_e  \n",
       "0        0     0  \n",
       "1        0     0  \n",
       "2        0     0  \n",
       "3        0     0  \n",
       "4        0     0  \n",
       "...    ...   ...  \n",
       "4204     0     0  \n",
       "4205     0     0  \n",
       "4206     0     0  \n",
       "4207     0     1  \n",
       "4208     0     0  \n",
       "\n",
       "[4209 rows x 16 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def one_hot_top_x(df,variable,top_x_labels):\n",
    "    for label in top_x_labels:\n",
    "        df[variable+'_'+label] = np.where(data[variable]==label,1,0)\n",
    "data = pd.read_csv('Mercedesdata.csv',usecols = ['X1', 'X2','X3','X4','X5','X6'])\n",
    "\n",
    "# Encode X2 into the 10 most frequent categories\n",
    "one_hot_top_x(data,'X2',top_10)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from scipy import stats\n",
    "#IQR1 = stats.iqr(y1, interpolation= 'midpoint')\n",
    "#y1_new = (y1-np.median(y1))/IQR1\n",
    "#IQR2 = stats.iqr(y2, interpolation= 'midpoint')\n",
    "#y2_new = (y2-np.median(y2))/IQR2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>396</td>\n",
       "      <td>324</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>397</td>\n",
       "      <td>325</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>398</td>\n",
       "      <td>330</td>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>399</td>\n",
       "      <td>312</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>400</td>\n",
       "      <td>333</td>\n",
       "      <td>117</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0             1        337          118                  4  4.5   4.5  9.65   \n",
       "1             2        324          107                  4  4.0   4.5  8.87   \n",
       "2             3        316          104                  3  3.0   3.5  8.00   \n",
       "3             4        322          110                  3  3.5   2.5  8.67   \n",
       "4             5        314          103                  2  2.0   3.0  8.21   \n",
       "..          ...        ...          ...                ...  ...   ...   ...   \n",
       "395         396        324          110                  3  3.5   3.5  9.04   \n",
       "396         397        325          107                  3  3.0   3.5  9.11   \n",
       "397         398        330          116                  4  5.0   4.5  9.45   \n",
       "398         399        312          103                  3  3.5   4.0  8.78   \n",
       "399         400        333          117                  4  5.0   4.0  9.66   \n",
       "\n",
       "     Research  Chance of Admit   \n",
       "0           1              0.92  \n",
       "1           1              0.76  \n",
       "2           1              0.72  \n",
       "3           1              0.80  \n",
       "4           0              0.65  \n",
       "..        ...               ...  \n",
       "395         1              0.82  \n",
       "396         1              0.84  \n",
       "397         1              0.91  \n",
       "398         0              0.67  \n",
       "399         1              0.95  \n",
       "\n",
       "[400 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Admission_Predict.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Serial No.', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>8.87</td>\n",
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       "      <td>0.76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>324</td>\n",
       "      <td>110</td>\n",
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       "      <td>9.04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>325</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>330</td>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>312</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>333</td>\n",
       "      <td>117</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  \\\n",
       "0          337          118                  4  4.5   4.5  9.65         1   \n",
       "1          324          107                  4  4.0   4.5  8.87         1   \n",
       "2          316          104                  3  3.0   3.5  8.00         1   \n",
       "3          322          110                  3  3.5   2.5  8.67         1   \n",
       "4          314          103                  2  2.0   3.0  8.21         0   \n",
       "..         ...          ...                ...  ...   ...   ...       ...   \n",
       "395        324          110                  3  3.5   3.5  9.04         1   \n",
       "396        325          107                  3  3.0   3.5  9.11         1   \n",
       "397        330          116                  4  5.0   4.5  9.45         1   \n",
       "398        312          103                  3  3.5   4.0  8.78         0   \n",
       "399        333          117                  4  5.0   4.0  9.66         1   \n",
       "\n",
       "     Chance of Admit   \n",
       "0                0.92  \n",
       "1                0.76  \n",
       "2                0.72  \n",
       "3                0.80  \n",
       "4                0.65  \n",
       "..                ...  \n",
       "395              0.82  \n",
       "396              0.84  \n",
       "397              0.91  \n",
       "398              0.67  \n",
       "399              0.95  \n",
       "\n",
       "[400 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Chance of Admit ']\n",
    "df.drop(\"Chance of Admit \",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
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       "  <tbody>\n",
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       "      <th>209</th>\n",
       "      <td>301</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>311</td>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.64</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>340</td>\n",
       "      <td>114</td>\n",
       "      <td>5</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>325</td>\n",
       "      <td>108</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>301</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>316</td>\n",
       "      <td>105</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>312</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>301</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>329</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>313</td>\n",
       "      <td>109</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research\n",
       "209        301          104                  3  3.5   4.0  8.12         1\n",
       "280        311          102                  3  4.5   4.0  8.64         1\n",
       "33         340          114                  5  4.0   4.0  9.60         1\n",
       "210        325          108                  4  4.5   4.0  9.06         1\n",
       "93         301           97                  2  3.0   3.0  7.88         1\n",
       "..         ...          ...                ...  ...   ...   ...       ...\n",
       "246        316          105                  3  3.0   3.5  8.73         0\n",
       "227        312          110                  2  3.5   3.0  8.53         0\n",
       "369        301           98                  1  2.0   3.0  8.03         1\n",
       "176        329          119                  4  4.5   4.5  9.16         1\n",
       "289        313          109                  3  4.0   3.5  9.00         0\n",
       "\n",
       "[80 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>318</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>340</td>\n",
       "      <td>120</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.91</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>320</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>315</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>336</td>\n",
       "      <td>112</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>329</td>\n",
       "      <td>111</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>306</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>302</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>314</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research\n",
       "3          322          110                  3  3.5   2.5  8.67         1\n",
       "18         318          110                  3  4.0   3.0  8.80         0\n",
       "202        340          120                  5  4.5   4.5  9.91         1\n",
       "250        320          104                  3  3.0   2.5  8.57         1\n",
       "274        315          100                  1  2.0   2.5  7.95         0\n",
       "..         ...          ...                ...  ...   ...   ...       ...\n",
       "71         336          112                  5  5.0   5.0  9.76         1\n",
       "106        329          111                  4  4.5   4.5  9.18         1\n",
       "270        306          105                  2  2.5   3.0  8.22         1\n",
       "348        302           99                  1  2.0   2.0  7.25         0\n",
       "102        314          106                  2  4.0   3.5  8.25         0\n",
       "\n",
       "[320 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      0.80\n",
       "18     0.63\n",
       "202    0.97\n",
       "250    0.74\n",
       "274    0.58\n",
       "       ... \n",
       "71     0.96\n",
       "106    0.87\n",
       "270    0.72\n",
       "348    0.57\n",
       "102    0.62\n",
       "Name: Chance of Admit , Length: 320, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "209    0.68\n",
       "280    0.68\n",
       "33     0.90\n",
       "210    0.79\n",
       "93     0.44\n",
       "       ... \n",
       "246    0.72\n",
       "227    0.64\n",
       "369    0.67\n",
       "176    0.90\n",
       "289    0.79\n",
       "Name: Chance of Admit , Length: 80, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = pd.ExcelWriter('train.xlsx')\n",
    "x_train.to_excel(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.65117446, 0.72368741, 0.93536809, 0.82164316, 0.58158673,\n",
       "       0.92863016, 0.52682366, 0.54308993, 0.65940583, 0.83834924,\n",
       "       0.72008833, 0.90749769, 0.55354476, 0.89008648, 0.70389539,\n",
       "       0.68664473, 0.66657268, 0.48196096, 0.69057217, 0.97493132,\n",
       "       0.58802433, 0.65286881, 0.71150098, 0.53528647, 0.94677007,\n",
       "       0.80982947, 0.69459383, 0.56495613, 0.68192423, 0.81039878,\n",
       "       0.80796481, 0.94640983, 0.64599494, 0.51104918, 0.65983663,\n",
       "       0.66907811, 0.71572271, 0.64556878, 0.61540702, 0.87367833,\n",
       "       0.74275261, 0.59782649, 0.77456683, 0.95944897, 0.85124125,\n",
       "       0.83554825, 0.94662422, 0.64822919, 0.92247594, 0.85906183,\n",
       "       0.89624998, 0.72869743, 0.78874783, 0.95142703, 0.57325803,\n",
       "       0.58744723, 0.68621316, 0.84544646, 0.60495144, 0.84808919,\n",
       "       0.66642894, 0.65524969, 0.70024808, 0.51206905, 0.62930376,\n",
       "       0.7173701 , 0.62200838, 0.84170334, 0.85675802, 0.79886217,\n",
       "       0.72196478, 0.81448203, 0.87373609, 0.83332085, 0.52554247,\n",
       "       0.72181818, 0.6896438 , 0.59442609, 0.87840579, 0.75779333])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "pred = lr.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06794853476922812\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(df)\n",
    "df1 = sc.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.76210664,  1.74697064,  0.79882862, ...,  1.16732114,\n",
       "         1.76481828,  0.90911166],\n",
       "       [ 0.62765641, -0.06763531,  0.79882862, ...,  1.16732114,\n",
       "         0.45515126,  0.90911166],\n",
       "       [-0.07046681, -0.56252785, -0.07660001, ...,  0.05293342,\n",
       "        -1.00563118,  0.90911166],\n",
       "       ...,\n",
       "       [ 1.15124883,  1.41704229,  0.79882862, ...,  1.16732114,\n",
       "         1.42900622,  0.90911166],\n",
       "       [-0.41952842, -0.72749202, -0.07660001, ...,  0.61012728,\n",
       "         0.30403584, -1.09997489],\n",
       "       [ 1.41304503,  1.58200646,  0.79882862, ...,  0.61012728,\n",
       "         1.78160888,  0.90911166]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df1.shape)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(metrics.mean_squared_error())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[3 1]\n",
      " [2 4]]\n",
      "Accuracy: 0.7\n",
      "Precision: 0.8\n",
      "Recall: 0.6666666666666666\n",
      "F1-Score: 0.7272727272727272\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix\n",
    "# For Regression Algo\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error\n",
    " \n",
    "# Classification Metrics\n",
    "actual_labels =    [0, 1, 1, 0, 1, 0, 1, 0, 1, 1]\n",
    "predicted_labels = [0, 1, 0, 1, 1, 0, 1, 0, 0, 1]\n",
    " \n",
    "accuracy = accuracy_score(actual_labels,predicted_labels)\n",
    "precision = precision_score(actual_labels, predicted_labels)\n",
    "recall = recall_score(actual_labels, predicted_labels)\n",
    "f1 = f1_score(actual_labels, predicted_labels)\n",
    "conf_matrix = confusion_matrix(actual_labels, predicted_labels)\n",
    " \n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-Score:\", f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 2.2\n",
      "Mean Squared Error: 5.4\n",
      "R-squared: 0.892\n",
      "Median Absolute Error: 2.0\n"
     ]
    }
   ],
   "source": [
    "# Regression Metrics\n",
    "actual_values = [10, 15, 20, 25, 30]\n",
    "predicted_values = [12, 14, 18, 28, 33]\n",
    " \n",
    "mae = mean_absolute_error(actual_values, predicted_values)\n",
    "mse = mean_squared_error(actual_values, predicted_values)\n",
    "r2 = r2_score(actual_values, predicted_values)\n",
    "medae = median_absolute_error(actual_values, predicted_values)\n",
    " \n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)\n",
    "print(\"Median Absolute Error:\", medae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\"A\":[1,2,4,1,2,4], \n",
    "                    \"B\":[4,5,6,7,8,9], \n",
    "                    \"C\":[0,0,0,0,0,0],\n",
    "                    \"D\":[1,1,1,1,1,1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VarianceThreshold(threshold=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VarianceThreshold</label><div class=\"sk-toggleable__content\"><pre>VarianceThreshold(threshold=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "VarianceThreshold(threshold=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_thes = VarianceThreshold(threshold=0)\n",
    "var_thes.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_thes.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = data.columns[var_thes.get_support()].tolist()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'B'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_thes.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 371 columns</p>\n",
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       "         ID  var3  var15  imp_ent_var16_ult1  imp_op_var39_comer_ult1  \\\n",
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       "1         3     2     34                 0.0                     0.00   \n",
       "2         4     2     23                 0.0                     0.00   \n",
       "3         8     2     37                 0.0                   195.00   \n",
       "4        10     2     39                 0.0                     0.00   \n",
       "...     ...   ...    ...                 ...                      ...   \n",
       "9995  20069     2     52               150.0                     0.00   \n",
       "9996  20070     2     23                90.0                    51.06   \n",
       "9997  20071     2     37                 0.0                     0.00   \n",
       "9998  20072     2     24                 0.0                     0.00   \n",
       "9999  20074     2     40               585.0                     0.00   \n",
       "\n",
       "      imp_op_var39_comer_ult3  imp_op_var40_comer_ult1  \\\n",
       "0                        0.00                      0.0   \n",
       "1                        0.00                      0.0   \n",
       "2                        0.00                      0.0   \n",
       "3                      195.00                      0.0   \n",
       "4                        0.00                      0.0   \n",
       "...                       ...                      ...   \n",
       "9995                     0.00                      0.0   \n",
       "9996                    51.06                      0.0   \n",
       "9997                     0.00                      0.0   \n",
       "9998                     0.00                      0.0   \n",
       "9999                     0.00                      0.0   \n",
       "\n",
       "      imp_op_var40_comer_ult3  imp_op_var40_efect_ult1  \\\n",
       "0                         0.0                        0   \n",
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       "9995                      0.0                        0   \n",
       "9996                      0.0                        0   \n",
       "9997                      0.0                        0   \n",
       "9998                      0.0                        0   \n",
       "9999                      0.0                        0   \n",
       "\n",
       "      imp_op_var40_efect_ult3  ...  saldo_medio_var33_hace2  \\\n",
       "0                           0  ...                      0.0   \n",
       "1                           0  ...                      0.0   \n",
       "2                           0  ...                      0.0   \n",
       "3                           0  ...                      0.0   \n",
       "4                           0  ...                      0.0   \n",
       "...                       ...  ...                      ...   \n",
       "9995                        0  ...                      0.0   \n",
       "9996                        0  ...                      0.0   \n",
       "9997                        0  ...                      0.0   \n",
       "9998                        0  ...                      0.0   \n",
       "9999                        0  ...                      0.0   \n",
       "\n",
       "      saldo_medio_var33_hace3  saldo_medio_var33_ult1  saldo_medio_var33_ult3  \\\n",
       "0                         0.0                     0.0                     0.0   \n",
       "1                         0.0                     0.0                     0.0   \n",
       "2                         0.0                     0.0                     0.0   \n",
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       "4                         0.0                     0.0                     0.0   \n",
       "...                       ...                     ...                     ...   \n",
       "9995                      0.0                     0.0                     0.0   \n",
       "9996                      0.0                     0.0                     0.0   \n",
       "9997                      0.0                     0.0                     0.0   \n",
       "9998                      0.0                     0.0                     0.0   \n",
       "9999                      0.0                     0.0                     0.0   \n",
       "\n",
       "      saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
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       "2                         0.0                      0.0   \n",
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       "...                       ...                      ...   \n",
       "9995                      0.0                      0.0   \n",
       "9996                      0.0                      0.0   \n",
       "9997                      0.0                      0.0   \n",
       "9998                      0.0                      0.0   \n",
       "9999                      0.0                      0.0   \n",
       "\n",
       "      saldo_medio_var44_ult1  saldo_medio_var44_ult3          var38  TARGET  \n",
       "0                        0.0                     0.0   39205.170000       0  \n",
       "1                        0.0                     0.0   49278.030000       0  \n",
       "2                        0.0                     0.0   67333.770000       0  \n",
       "3                        0.0                     0.0   64007.970000       0  \n",
       "4                        0.0                     0.0  117310.979016       0  \n",
       "...                      ...                     ...            ...     ...  \n",
       "9995                     0.0                     0.0   27745.740000       1  \n",
       "9996                     0.0                     0.0  120468.090000       0  \n",
       "9997                     0.0                     0.0   98462.880000       0  \n",
       "9998                     0.0                     0.0  102070.770000       0  \n",
       "9999                     0.0                     0.0   55804.410000       0  \n",
       "\n",
       "[10000 rows x 371 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('santander.csv', nrows=10000)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49278.030000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67333.770000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64007.970000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117310.979016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 371 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  var3  var15  imp_ent_var16_ult1  imp_op_var39_comer_ult1  \\\n",
       "0   1     2     23                 0.0                      0.0   \n",
       "1   3     2     34                 0.0                      0.0   \n",
       "2   4     2     23                 0.0                      0.0   \n",
       "3   8     2     37                 0.0                    195.0   \n",
       "4  10     2     39                 0.0                      0.0   \n",
       "\n",
       "   imp_op_var39_comer_ult3  imp_op_var40_comer_ult1  imp_op_var40_comer_ult3  \\\n",
       "0                      0.0                      0.0                      0.0   \n",
       "1                      0.0                      0.0                      0.0   \n",
       "2                      0.0                      0.0                      0.0   \n",
       "3                    195.0                      0.0                      0.0   \n",
       "4                      0.0                      0.0                      0.0   \n",
       "\n",
       "   imp_op_var40_efect_ult1  imp_op_var40_efect_ult3  ...  \\\n",
       "0                        0                        0  ...   \n",
       "1                        0                        0  ...   \n",
       "2                        0                        0  ...   \n",
       "3                        0                        0  ...   \n",
       "4                        0                        0  ...   \n",
       "\n",
       "   saldo_medio_var33_hace2  saldo_medio_var33_hace3  saldo_medio_var33_ult1  \\\n",
       "0                      0.0                      0.0                     0.0   \n",
       "1                      0.0                      0.0                     0.0   \n",
       "2                      0.0                      0.0                     0.0   \n",
       "3                      0.0                      0.0                     0.0   \n",
       "4                      0.0                      0.0                     0.0   \n",
       "\n",
       "   saldo_medio_var33_ult3  saldo_medio_var44_hace2  saldo_medio_var44_hace3  \\\n",
       "0                     0.0                      0.0                      0.0   \n",
       "1                     0.0                      0.0                      0.0   \n",
       "2                     0.0                      0.0                      0.0   \n",
       "3                     0.0                      0.0                      0.0   \n",
       "4                     0.0                      0.0                      0.0   \n",
       "\n",
       "   saldo_medio_var44_ult1  saldo_medio_var44_ult3          var38  TARGET  \n",
       "0                     0.0                     0.0   39205.170000       0  \n",
       "1                     0.0                     0.0   49278.030000       0  \n",
       "2                     0.0                     0.0   67333.770000       0  \n",
       "3                     0.0                     0.0   64007.970000       0  \n",
       "4                     0.0                     0.0  117310.979016       0  \n",
       "\n",
       "[5 rows x 371 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 371)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(labels=['TARGET'], axis=1)\n",
    "y = df['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 370), (3000, 370))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VarianceThreshold(threshold=5054)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VarianceThreshold</label><div class=\"sk-toggleable__content\"><pre>VarianceThreshold(threshold=5054)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "VarianceThreshold(threshold=5054)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_thes = VarianceThreshold(threshold=5054)\n",
    "var_thes.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True,  True,  True,  True,  True, False,\n",
       "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "       False,  True,  True,  True, False,  True,  True,  True,  True,\n",
       "       False, False,  True,  True,  True,  True,  True, False,  True,\n",
       "        True, False,  True,  True, False, False, False, False,  True,\n",
       "        True, False,  True,  True, False, False,  True, False,  True,\n",
       "       False,  True,  True,  True, False,  True,  True, False, False,\n",
       "        True, False,  True, False,  True, False, False, False, False,\n",
       "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
       "        True, False,  True, False, False, False, False,  True,  True,\n",
       "       False,  True, False, False, False, False, False, False, False,\n",
       "        True, False, False, False, False, False, False, False, False,\n",
       "        True, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "       False, False,  True, False,  True,  True, False, False, False,\n",
       "       False,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_thes.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(var_thes.get_support())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
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       "      <td>1</td>\n",
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       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
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       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
       "\n",
       "     ptratio       b  lstat  medv  Unnamed: 14  Unnamed: 15  Unnamed: 16  \n",
       "0       15.3  396.90   4.98  24.0          NaN          NaN          NaN  \n",
       "1       17.8  396.90   9.14  21.6          NaN          NaN          NaN  \n",
       "2       17.8  392.83   4.03  34.7          NaN          NaN          NaN  \n",
       "3       18.7  394.63   2.94  33.4          NaN          NaN          NaN  \n",
       "4       18.7  396.90   5.33  36.2          NaN          NaN          NaN  \n",
       "..       ...     ...    ...   ...          ...          ...          ...  \n",
       "501     21.0  391.99   9.67  22.4          NaN          NaN          NaN  \n",
       "502     21.0  396.90   9.08  20.6          NaN          NaN          NaN  \n",
       "503     21.0  396.90   5.64  23.9          NaN          NaN          NaN  \n",
       "504     21.0  393.45   6.48  22.0          NaN          NaN          NaN  \n",
       "505     21.0  396.90   7.88  11.9          NaN          NaN          NaN  \n",
       "\n",
       "[506 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('boston.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        b  lstat  medv  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(\"medv\",axis=1)\n",
    "y = df[\"medv\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        b  lstat  medv  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "1    21.6\n",
       "2    34.7\n",
       "3    33.4\n",
       "4    36.2\n",
       "Name: medv, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((354, 13), (152, 13))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crim</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196172</td>\n",
       "      <td>0.382073</td>\n",
       "      <td>-0.049364</td>\n",
       "      <td>0.416560</td>\n",
       "      <td>-0.188280</td>\n",
       "      <td>0.329927</td>\n",
       "      <td>-0.355840</td>\n",
       "      <td>0.603880</td>\n",
       "      <td>0.560570</td>\n",
       "      <td>0.264780</td>\n",
       "      <td>-0.299525</td>\n",
       "      <td>0.439369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zn</th>\n",
       "      <td>-0.196172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.529392</td>\n",
       "      <td>-0.063863</td>\n",
       "      <td>-0.523572</td>\n",
       "      <td>0.319260</td>\n",
       "      <td>-0.583885</td>\n",
       "      <td>0.658331</td>\n",
       "      <td>-0.314833</td>\n",
       "      <td>-0.327834</td>\n",
       "      <td>-0.392838</td>\n",
       "      <td>0.164641</td>\n",
       "      <td>-0.429178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indus</th>\n",
       "      <td>0.382073</td>\n",
       "      <td>-0.529392</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044224</td>\n",
       "      <td>0.750218</td>\n",
       "      <td>-0.392969</td>\n",
       "      <td>0.629257</td>\n",
       "      <td>-0.686848</td>\n",
       "      <td>0.578459</td>\n",
       "      <td>0.719038</td>\n",
       "      <td>0.388353</td>\n",
       "      <td>-0.331638</td>\n",
       "      <td>0.603374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chas</th>\n",
       "      <td>-0.049364</td>\n",
       "      <td>-0.063863</td>\n",
       "      <td>0.044224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043748</td>\n",
       "      <td>0.088125</td>\n",
       "      <td>0.067269</td>\n",
       "      <td>-0.085492</td>\n",
       "      <td>0.022338</td>\n",
       "      <td>-0.017156</td>\n",
       "      <td>-0.072683</td>\n",
       "      <td>0.069682</td>\n",
       "      <td>-0.059060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nox</th>\n",
       "      <td>0.416560</td>\n",
       "      <td>-0.523572</td>\n",
       "      <td>0.750218</td>\n",
       "      <td>0.043748</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.279202</td>\n",
       "      <td>0.740052</td>\n",
       "      <td>-0.765753</td>\n",
       "      <td>0.627188</td>\n",
       "      <td>0.683445</td>\n",
       "      <td>0.179046</td>\n",
       "      <td>-0.369445</td>\n",
       "      <td>0.577154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm</th>\n",
       "      <td>-0.188280</td>\n",
       "      <td>0.319260</td>\n",
       "      <td>-0.392969</td>\n",
       "      <td>0.088125</td>\n",
       "      <td>-0.279202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.235839</td>\n",
       "      <td>0.183857</td>\n",
       "      <td>-0.179242</td>\n",
       "      <td>-0.275242</td>\n",
       "      <td>-0.385526</td>\n",
       "      <td>0.157459</td>\n",
       "      <td>-0.623920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.329927</td>\n",
       "      <td>-0.583885</td>\n",
       "      <td>0.629257</td>\n",
       "      <td>0.067269</td>\n",
       "      <td>0.740052</td>\n",
       "      <td>-0.235839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.761543</td>\n",
       "      <td>0.440578</td>\n",
       "      <td>0.502429</td>\n",
       "      <td>0.239729</td>\n",
       "      <td>-0.250416</td>\n",
       "      <td>0.606530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.355840</td>\n",
       "      <td>0.658331</td>\n",
       "      <td>-0.686848</td>\n",
       "      <td>-0.085492</td>\n",
       "      <td>-0.765753</td>\n",
       "      <td>0.183857</td>\n",
       "      <td>-0.761543</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.467653</td>\n",
       "      <td>-0.519643</td>\n",
       "      <td>-0.176620</td>\n",
       "      <td>0.248376</td>\n",
       "      <td>-0.501780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rad</th>\n",
       "      <td>0.603880</td>\n",
       "      <td>-0.314833</td>\n",
       "      <td>0.578459</td>\n",
       "      <td>0.022338</td>\n",
       "      <td>0.627188</td>\n",
       "      <td>-0.179242</td>\n",
       "      <td>0.440578</td>\n",
       "      <td>-0.467653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907455</td>\n",
       "      <td>0.437687</td>\n",
       "      <td>-0.415325</td>\n",
       "      <td>0.442783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax</th>\n",
       "      <td>0.560570</td>\n",
       "      <td>-0.327834</td>\n",
       "      <td>0.719038</td>\n",
       "      <td>-0.017156</td>\n",
       "      <td>0.683445</td>\n",
       "      <td>-0.275242</td>\n",
       "      <td>0.502429</td>\n",
       "      <td>-0.519643</td>\n",
       "      <td>0.907455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.447518</td>\n",
       "      <td>-0.412145</td>\n",
       "      <td>0.515905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptratio</th>\n",
       "      <td>0.264780</td>\n",
       "      <td>-0.392838</td>\n",
       "      <td>0.388353</td>\n",
       "      <td>-0.072683</td>\n",
       "      <td>0.179046</td>\n",
       "      <td>-0.385526</td>\n",
       "      <td>0.239729</td>\n",
       "      <td>-0.176620</td>\n",
       "      <td>0.437687</td>\n",
       "      <td>0.447518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.145638</td>\n",
       "      <td>0.387752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.299525</td>\n",
       "      <td>0.164641</td>\n",
       "      <td>-0.331638</td>\n",
       "      <td>0.069682</td>\n",
       "      <td>-0.369445</td>\n",
       "      <td>0.157459</td>\n",
       "      <td>-0.250416</td>\n",
       "      <td>0.248376</td>\n",
       "      <td>-0.415325</td>\n",
       "      <td>-0.412145</td>\n",
       "      <td>-0.145638</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.365338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstat</th>\n",
       "      <td>0.439369</td>\n",
       "      <td>-0.429178</td>\n",
       "      <td>0.603374</td>\n",
       "      <td>-0.059060</td>\n",
       "      <td>0.577154</td>\n",
       "      <td>-0.623920</td>\n",
       "      <td>0.606530</td>\n",
       "      <td>-0.501780</td>\n",
       "      <td>0.442783</td>\n",
       "      <td>0.515905</td>\n",
       "      <td>0.387752</td>\n",
       "      <td>-0.365338</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             crim        zn     indus      chas       nox        rm       age  \\\n",
       "crim     1.000000 -0.196172  0.382073 -0.049364  0.416560 -0.188280  0.329927   \n",
       "zn      -0.196172  1.000000 -0.529392 -0.063863 -0.523572  0.319260 -0.583885   \n",
       "indus    0.382073 -0.529392  1.000000  0.044224  0.750218 -0.392969  0.629257   \n",
       "chas    -0.049364 -0.063863  0.044224  1.000000  0.043748  0.088125  0.067269   \n",
       "nox      0.416560 -0.523572  0.750218  0.043748  1.000000 -0.279202  0.740052   \n",
       "rm      -0.188280  0.319260 -0.392969  0.088125 -0.279202  1.000000 -0.235839   \n",
       "age      0.329927 -0.583885  0.629257  0.067269  0.740052 -0.235839  1.000000   \n",
       "dis     -0.355840  0.658331 -0.686848 -0.085492 -0.765753  0.183857 -0.761543   \n",
       "rad      0.603880 -0.314833  0.578459  0.022338  0.627188 -0.179242  0.440578   \n",
       "tax      0.560570 -0.327834  0.719038 -0.017156  0.683445 -0.275242  0.502429   \n",
       "ptratio  0.264780 -0.392838  0.388353 -0.072683  0.179046 -0.385526  0.239729   \n",
       "b       -0.299525  0.164641 -0.331638  0.069682 -0.369445  0.157459 -0.250416   \n",
       "lstat    0.439369 -0.429178  0.603374 -0.059060  0.577154 -0.623920  0.606530   \n",
       "\n",
       "              dis       rad       tax   ptratio         b     lstat  \n",
       "crim    -0.355840  0.603880  0.560570  0.264780 -0.299525  0.439369  \n",
       "zn       0.658331 -0.314833 -0.327834 -0.392838  0.164641 -0.429178  \n",
       "indus   -0.686848  0.578459  0.719038  0.388353 -0.331638  0.603374  \n",
       "chas    -0.085492  0.022338 -0.017156 -0.072683  0.069682 -0.059060  \n",
       "nox     -0.765753  0.627188  0.683445  0.179046 -0.369445  0.577154  \n",
       "rm       0.183857 -0.179242 -0.275242 -0.385526  0.157459 -0.623920  \n",
       "age     -0.761543  0.440578  0.502429  0.239729 -0.250416  0.606530  \n",
       "dis      1.000000 -0.467653 -0.519643 -0.176620  0.248376 -0.501780  \n",
       "rad     -0.467653  1.000000  0.907455  0.437687 -0.415325  0.442783  \n",
       "tax     -0.519643  0.907455  1.000000  0.447518 -0.412145  0.515905  \n",
       "ptratio -0.176620  0.437687  0.447518  1.000000 -0.145638  0.387752  \n",
       "b        0.248376 -0.415325 -0.412145 -0.145638  1.000000 -0.365338  \n",
       "lstat   -0.501780  0.442783  0.515905  0.387752 -0.365338  1.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(12,10))\n",
    "cor = x_train.corr()\n",
    "sns.heatmap(cor,annot=True,cmap=plt.cm.CMRmap_r)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(dataset.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold:\n",
    "                colname = corr_matrix.columns[i]\n",
    "                col_corr.add(colname)\n",
    "    return col_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = correlation(x_train, 0.7)\n",
    "len(set(corr_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age', 'dis', 'nox', 'tax'}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('santander.csv', nrows=10000)\n",
    "x = df.drop(labels=['TARGET'], axis=1)\n",
    "y = df['TARGET']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = x_train.corr()\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11,11)\n",
    "sns.heatmap(corrmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = correlation(x_train, 0.9)\n",
    "len(set(corr_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chisquare Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('titanic')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      sex embarked  alone  pclass  survived\n",
       "0    male        S  False       3         0\n",
       "1  female        C  False       1         1\n",
       "2  female        S   True       3         1\n",
       "3  female        S  False       1         1\n",
       "4    male        S   True       3         0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['sex', 'embarked', 'alone', 'pclass', 'survived']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91629\\AppData\\Local\\Temp\\ipykernel_10500\\2096163075.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['sex'] = np.where(df['sex']=='male',1,0)\n"
     ]
    },
    {
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      "text/plain": [
       "   sex embarked  alone  pclass  survived\n",
       "0    1        S  False       3         0\n",
       "1    0        C  False       1         1\n",
       "2    0        S   True       3         1\n",
       "3    0        S  False       1         1\n",
       "4    1        S   True       3         0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['sex'] = np.where(df['sex']=='male',1,0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91629\\AppData\\Local\\Temp\\ipykernel_10500\\1503162440.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['embarked'] = df['embarked'].map(ordinal_label)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>embarked</th>\n",
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       "      <th>pclass</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  embarked  alone  pclass  survived\n",
       "0    1         0  False       3         0\n",
       "1    0         1  False       1         1\n",
       "2    0         0   True       3         1\n",
       "3    0         0  False       1         1\n",
       "4    1         0   True       3         0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_label = {k: i for i, k in enumerate(df['embarked'].unique(),0)}\n",
    "ordinal_label \n",
    "df['embarked'] = df['embarked'].map(ordinal_label)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91629\\AppData\\Local\\Temp\\ipykernel_10500\\2247494899.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['alone'] = np.where(df['alone']=='True',1,0)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>embarked</th>\n",
       "      <th>alone</th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  embarked  alone  pclass  survived\n",
       "0    1         0      0       3         0\n",
       "1    0         1      0       1         1\n",
       "2    0         0      0       3         1\n",
       "3    0         0      0       1         1\n",
       "4    1         0      0       3         0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['alone'] = np.where(df['alone']=='True',1,0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex         0\n",
       "embarked    0\n",
       "alone       0\n",
       "pclass      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df[['sex', 'embarked', 'alone', 'pclass']], df['survived'], test_size=0.3, random_state=0)\n",
    "\n",
    "x_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([63.55447864, 11.83961845,         nan, 21.61080949]),\n",
       " array([1.55992554e-15, 5.79837058e-04,            nan, 3.33964360e-06]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "f_p_values=chi2(x_train, y_train)\n",
    "f_p_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MoviePy - Writing audio in Baby Shark Audio.mp3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MoviePy - Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "import moviepy.editor\n",
    "cvt_video = moviepy.editor.VideoFileClip(\"WhatsApp Video 2024-08-03 at 12.20.53_6064dbea.mp4\")\n",
    "ext_audio = cvt_video.audio\n",
    "ext_audio.write_audiofile(\"Baby Shark Audio.mp3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  }
 ],
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