KNN Regressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Real_Combine.csv')"
   ]
  },
  {
   "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>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>219.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>12.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1019.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>154.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.6</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>223.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1017.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>200.645833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      T    TM   Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0   7.4   9.8  4.8  1017.6  93.0  0.5  4.3   9.4  219.720833\n",
       "1   7.8  12.7  4.4  1018.5  87.0  0.6  4.4  11.1  182.187500\n",
       "2   6.7  13.4  2.4  1019.4  82.0  0.6  4.8  11.1  154.037500\n",
       "3   8.6  15.5  3.3  1018.7  72.0  0.8  8.1  20.6  223.208333\n",
       "4  12.4  20.9  4.4  1017.3  61.0  1.3  8.7  22.2  200.645833"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Check for Null Values\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=True,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1] ##Independent Features\n",
    "y=df.iloc[:,-1]  ##Dependent Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</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",
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       "      <td>False</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",
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       "      <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",
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       "    <tr>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>12</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",
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       "    <tr>\n",
       "      <th>13</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",
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       "    <tr>\n",
       "      <th>14</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</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",
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       "    <tr>\n",
       "      <th>16</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",
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       "    <tr>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1092 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          T     TM     Tm    SLP      H     VV      V     VM\n",
       "0     False  False  False  False  False  False  False  False\n",
       "1     False  False  False  False  False  False  False  False\n",
       "2     False  False  False  False  False  False  False  False\n",
       "3     False  False  False  False  False  False  False  False\n",
       "4     False  False  False  False  False  False  False  False\n",
       "5     False  False  False  False  False  False  False  False\n",
       "6     False  False  False  False  False  False  False  False\n",
       "7     False  False  False  False  False  False  False  False\n",
       "8     False  False  False  False  False  False  False  False\n",
       "9     False  False  False  False  False  False  False  False\n",
       "10    False  False  False  False  False  False  False  False\n",
       "11    False  False  False  False  False  False  False  False\n",
       "12    False  False  False  False  False  False  False  False\n",
       "13    False  False  False  False  False  False  False  False\n",
       "14    False  False  False  False  False  False  False  False\n",
       "15    False  False  False  False  False  False  False  False\n",
       "16    False  False  False  False  False  False  False  False\n",
       "17    False  False  False  False  False  False  False  False\n",
       "18    False  False  False  False  False  False  False  False\n",
       "19    False  False  False  False  False  False  False  False\n",
       "20    False  False  False  False  False  False  False  False\n",
       "21    False  False  False  False  False  False  False  False\n",
       "22    False  False  False  False  False  False  False  False\n",
       "23    False  False  False  False  False  False  False  False\n",
       "24    False  False  False  False  False  False  False  False\n",
       "25    False  False  False  False  False  False  False  False\n",
       "26    False  False  False  False  False  False  False  False\n",
       "27    False  False  False  False  False  False  False  False\n",
       "28    False  False  False  False  False  False  False  False\n",
       "29    False  False  False  False  False  False  False  False\n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...\n",
       "1063  False  False  False  False  False  False  False  False\n",
       "1064  False  False  False  False  False  False  False  False\n",
       "1065  False  False  False  False  False  False  False  False\n",
       "1066  False  False  False  False  False  False  False  False\n",
       "1067  False  False  False  False  False  False  False  False\n",
       "1068  False  False  False  False  False  False  False  False\n",
       "1069  False  False  False  False  False  False  False  False\n",
       "1070  False  False  False  False  False  False  False  False\n",
       "1071  False  False  False  False  False  False  False  False\n",
       "1072  False  False  False  False  False  False  False  False\n",
       "1073  False  False  False  False  False  False  False  False\n",
       "1074  False  False  False  False  False  False  False  False\n",
       "1075  False  False  False  False  False  False  False  False\n",
       "1076  False  False  False  False  False  False  False  False\n",
       "1077  False  False  False  False  False  False  False  False\n",
       "1078  False  False  False  False  False  False  False  False\n",
       "1079  False  False  False  False  False  False  False  False\n",
       "1080  False  False  False  False  False  False  False  False\n",
       "1081  False  False  False  False  False  False  False  False\n",
       "1082  False  False  False  False  False  False  False  False\n",
       "1083  False  False  False  False  False  False  False  False\n",
       "1084  False  False  False  False  False  False  False  False\n",
       "1085  False  False  False  False  False  False  False  False\n",
       "1086  False  False  False  False  False  False  False  False\n",
       "1087  False  False  False  False  False  False  False  False\n",
       "1088  False  False  False  False  False  False  False  False\n",
       "1089  False  False  False  False  False  False  False  False\n",
       "1090  False  False  False  False  False  False  False  False\n",
       "1091  False  False  False  False  False  False  False  False\n",
       "1092  False  False  False  False  False  False  False  False\n",
       "\n",
       "[1092 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Check Null Values\n",
    "X.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "5       False\n",
       "6       False\n",
       "7       False\n",
       "8       False\n",
       "9       False\n",
       "10      False\n",
       "11      False\n",
       "12      False\n",
       "13      False\n",
       "14      False\n",
       "15      False\n",
       "16      False\n",
       "17      False\n",
       "18      False\n",
       "19      False\n",
       "20      False\n",
       "21      False\n",
       "22      False\n",
       "23      False\n",
       "24      False\n",
       "25      False\n",
       "26      False\n",
       "27      False\n",
       "28      False\n",
       "29      False\n",
       "        ...  \n",
       "1063    False\n",
       "1064    False\n",
       "1065    False\n",
       "1066    False\n",
       "1067    False\n",
       "1068    False\n",
       "1069    False\n",
       "1070    False\n",
       "1071    False\n",
       "1072    False\n",
       "1073    False\n",
       "1074    False\n",
       "1075    False\n",
       "1076    False\n",
       "1077    False\n",
       "1078    False\n",
       "1079    False\n",
       "1080    False\n",
       "1081    False\n",
       "1082    False\n",
       "1083    False\n",
       "1084    False\n",
       "1085    False\n",
       "1086    False\n",
       "1087    False\n",
       "1088    False\n",
       "1089    False\n",
       "1090    False\n",
       "1091    False\n",
       "1092    False\n",
       "Name: PM 2.5, Length: 1092, dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967536</td>\n",
       "      <td>0.953719</td>\n",
       "      <td>-0.881409</td>\n",
       "      <td>-0.509299</td>\n",
       "      <td>0.640792</td>\n",
       "      <td>0.301994</td>\n",
       "      <td>0.287738</td>\n",
       "      <td>-0.631462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM</th>\n",
       "      <td>0.967536</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892031</td>\n",
       "      <td>-0.822958</td>\n",
       "      <td>-0.586681</td>\n",
       "      <td>0.606945</td>\n",
       "      <td>0.292949</td>\n",
       "      <td>0.297011</td>\n",
       "      <td>-0.568409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tm</th>\n",
       "      <td>0.953719</td>\n",
       "      <td>0.892031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.917518</td>\n",
       "      <td>-0.287357</td>\n",
       "      <td>0.577240</td>\n",
       "      <td>0.296225</td>\n",
       "      <td>0.266782</td>\n",
       "      <td>-0.673824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLP</th>\n",
       "      <td>-0.881409</td>\n",
       "      <td>-0.822958</td>\n",
       "      <td>-0.917518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.240256</td>\n",
       "      <td>-0.517915</td>\n",
       "      <td>-0.329838</td>\n",
       "      <td>-0.310704</td>\n",
       "      <td>0.623187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.509299</td>\n",
       "      <td>-0.586681</td>\n",
       "      <td>-0.287357</td>\n",
       "      <td>0.240256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.465374</td>\n",
       "      <td>-0.380575</td>\n",
       "      <td>-0.362177</td>\n",
       "      <td>0.138005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VV</th>\n",
       "      <td>0.640792</td>\n",
       "      <td>0.606945</td>\n",
       "      <td>0.577240</td>\n",
       "      <td>-0.517915</td>\n",
       "      <td>-0.465374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.376873</td>\n",
       "      <td>0.342442</td>\n",
       "      <td>-0.573941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0.301994</td>\n",
       "      <td>0.292949</td>\n",
       "      <td>0.296225</td>\n",
       "      <td>-0.329838</td>\n",
       "      <td>-0.380575</td>\n",
       "      <td>0.376873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.775655</td>\n",
       "      <td>-0.268530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VM</th>\n",
       "      <td>0.287738</td>\n",
       "      <td>0.297011</td>\n",
       "      <td>0.266782</td>\n",
       "      <td>-0.310704</td>\n",
       "      <td>-0.362177</td>\n",
       "      <td>0.342442</td>\n",
       "      <td>0.775655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.215854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM 2.5</th>\n",
       "      <td>-0.631462</td>\n",
       "      <td>-0.568409</td>\n",
       "      <td>-0.673824</td>\n",
       "      <td>0.623187</td>\n",
       "      <td>0.138005</td>\n",
       "      <td>-0.573941</td>\n",
       "      <td>-0.268530</td>\n",
       "      <td>-0.215854</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T        TM        Tm       SLP         H        VV         V  \\\n",
       "T       1.000000  0.967536  0.953719 -0.881409 -0.509299  0.640792  0.301994   \n",
       "TM      0.967536  1.000000  0.892031 -0.822958 -0.586681  0.606945  0.292949   \n",
       "Tm      0.953719  0.892031  1.000000 -0.917518 -0.287357  0.577240  0.296225   \n",
       "SLP    -0.881409 -0.822958 -0.917518  1.000000  0.240256 -0.517915 -0.329838   \n",
       "H      -0.509299 -0.586681 -0.287357  0.240256  1.000000 -0.465374 -0.380575   \n",
       "VV      0.640792  0.606945  0.577240 -0.517915 -0.465374  1.000000  0.376873   \n",
       "V       0.301994  0.292949  0.296225 -0.329838 -0.380575  0.376873  1.000000   \n",
       "VM      0.287738  0.297011  0.266782 -0.310704 -0.362177  0.342442  0.775655   \n",
       "PM 2.5 -0.631462 -0.568409 -0.673824  0.623187  0.138005 -0.573941 -0.268530   \n",
       "\n",
       "              VM    PM 2.5  \n",
       "T       0.287738 -0.631462  \n",
       "TM      0.297011 -0.568409  \n",
       "Tm      0.266782 -0.673824  \n",
       "SLP    -0.310704  0.623187  \n",
       "H      -0.362177  0.138005  \n",
       "VV      0.342442 -0.573941  \n",
       "V       0.775655 -0.268530  \n",
       "VM      1.000000 -0.215854  \n",
       "PM 2.5 -0.215854  1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get Correlations of each feature in dataset\n",
    "corrmat=df.corr()\n",
    "top_corr_features=corrmat.index\n",
    "plt.figure(figsize=(20,20))\n",
    "#Plot Heat Map\n",
    "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor=KNeighborsRegressor(n_neighbors=1)\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of Determination of R^2 on train set : 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficient of Determination of R^2 on train set : {}\".format(regressor.score(X_train,y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of Determination of R^2 on test set : 0.5417545333477789\n"
     ]
    }
   ],
   "source": [
    "print(\"Coefficient of Determination of R^2 on test set : {}\".format(regressor.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(regressor,X,y,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39387489016756577"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PM 2.5'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22f5f0ea128>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HyperParameter Tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_rate=[]\n",
    "\n",
    "#Will take Some time\n",
    "\n",
    "for i in range(1,40):\n",
    "    knn=KNeighborsRegressor(n_neighbors=i)\n",
    "    score=cross_val_score(knn,X,y,cv=10,scoring='neg_mean_squared_error')\n",
    "    accuracy_rate.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy Rate')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),accuracy_rate,color='blue',linestyle='dashed',marker='o',\n",
    "        markerfacecolor='red',markersize=10)\n",
    "plt.title('Accuracy Rate vs. K value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Accuracy Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Choosing the value of k as 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsRegressor(n_neighbors=2)\n",
    "knn.fit(X_train,y_train)\n",
    "predictions=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PM 2.5'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22f60699550>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE : 30.499942835365857\n",
      "MSE : 3269.0729583624457\n",
      "RMSE : 57.17580745702194\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "print('MAE :',metrics.mean_absolute_error(y_test,predictions))\n",
    "print('MSE :',metrics.mean_squared_error(y_test,predictions))\n",
    "print(\"RMSE :\",np.sqrt(metrics.mean_squared_error(y_test,predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}