final_presentation - losAngelesData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"color: blue; text-align:center;\">LINEAR REGRESSION - Los Angeles</h1>\n",
    "<p>Predicting temperatures according to pollution gases</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>1. Importing Libraries</h3>"
   ]
  },
  {
   "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",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2. Reading the data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>40.1056</td>\n",
       "      <td>1.2726</td>\n",
       "      <td>1.0665</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>37.9475</td>\n",
       "      <td>2.4532</td>\n",
       "      <td>1.1439</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>31.6259</td>\n",
       "      <td>2.3950</td>\n",
       "      <td>0.9490</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>33.6259</td>\n",
       "      <td>2.0295</td>\n",
       "      <td>0.9590</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>25.1765</td>\n",
       "      <td>2.6180</td>\n",
       "      <td>0.5942</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      NO2     SO2      CO      O3  Temperature\n",
       "0        2000  40.1056  1.2726  1.0665  0.0177         62.4\n",
       "1        2001  37.9475  2.4532  1.1439  0.0189         61.1\n",
       "2        2002  31.6259  2.3950  0.9490  0.0167         59.5\n",
       "3        2003  33.6259  2.0295  0.9590  0.0187         61.7\n",
       "4        2004  25.1765  2.6180  0.5942  0.0281         62.6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/los_angeles_data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3. Analyzing the data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.500000</td>\n",
       "      <td>24.284512</td>\n",
       "      <td>1.390219</td>\n",
       "      <td>0.600575</td>\n",
       "      <td>0.022550</td>\n",
       "      <td>62.306250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.760952</td>\n",
       "      <td>7.293196</td>\n",
       "      <td>1.064331</td>\n",
       "      <td>0.266688</td>\n",
       "      <td>0.003264</td>\n",
       "      <td>1.403077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>18.146700</td>\n",
       "      <td>0.265700</td>\n",
       "      <td>0.377000</td>\n",
       "      <td>0.016700</td>\n",
       "      <td>59.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.750000</td>\n",
       "      <td>19.575750</td>\n",
       "      <td>0.369900</td>\n",
       "      <td>0.409975</td>\n",
       "      <td>0.021450</td>\n",
       "      <td>61.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2007.500000</td>\n",
       "      <td>20.947250</td>\n",
       "      <td>1.151200</td>\n",
       "      <td>0.492000</td>\n",
       "      <td>0.022750</td>\n",
       "      <td>62.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2011.250000</td>\n",
       "      <td>26.788850</td>\n",
       "      <td>2.120875</td>\n",
       "      <td>0.682900</td>\n",
       "      <td>0.023800</td>\n",
       "      <td>62.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>40.105600</td>\n",
       "      <td>3.715700</td>\n",
       "      <td>1.143900</td>\n",
       "      <td>0.028100</td>\n",
       "      <td>65.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0        NO2        SO2         CO         O3  Temperature\n",
       "count    16.000000  16.000000  16.000000  16.000000  16.000000    16.000000\n",
       "mean   2007.500000  24.284512   1.390219   0.600575   0.022550    62.306250\n",
       "std       4.760952   7.293196   1.064331   0.266688   0.003264     1.403077\n",
       "min    2000.000000  18.146700   0.265700   0.377000   0.016700    59.500000\n",
       "25%    2003.750000  19.575750   0.369900   0.409975   0.021450    61.525000\n",
       "50%    2007.500000  20.947250   1.151200   0.492000   0.022750    62.350000\n",
       "75%    2011.250000  26.788850   2.120875   0.682900   0.023800    62.775000\n",
       "max    2015.000000  40.105600   3.715700   1.143900   0.028100    65.300000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Unnamed: 0   16 non-null     int64  \n",
      " 1   NO2          16 non-null     float64\n",
      " 2   SO2          16 non-null     float64\n",
      " 3   CO           16 non-null     float64\n",
      " 4   O3           16 non-null     float64\n",
      " 5   Temperature  16 non-null     float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 896.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     0\n",
       "NO2            0\n",
       "SO2            0\n",
       "CO             0\n",
       "O3             0\n",
       "Temperature    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>4. Cleaning the data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>40.1056</td>\n",
       "      <td>1.2726</td>\n",
       "      <td>1.0665</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>37.9475</td>\n",
       "      <td>2.4532</td>\n",
       "      <td>1.1439</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>31.6259</td>\n",
       "      <td>2.3950</td>\n",
       "      <td>0.9490</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>33.6259</td>\n",
       "      <td>2.0295</td>\n",
       "      <td>0.9590</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>25.1765</td>\n",
       "      <td>2.6180</td>\n",
       "      <td>0.5942</td>\n",
       "      <td>0.0281</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>20.0479</td>\n",
       "      <td>3.7157</td>\n",
       "      <td>0.4351</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>62.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>21.9860</td>\n",
       "      <td>1.9429</td>\n",
       "      <td>0.3837</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>21.4902</td>\n",
       "      <td>1.8018</td>\n",
       "      <td>0.3841</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>61.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>20.4043</td>\n",
       "      <td>0.9937</td>\n",
       "      <td>0.3770</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>21.8176</td>\n",
       "      <td>1.0298</td>\n",
       "      <td>0.5203</td>\n",
       "      <td>0.0229</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>18.6410</td>\n",
       "      <td>0.4666</td>\n",
       "      <td>0.4491</td>\n",
       "      <td>0.0226</td>\n",
       "      <td>61.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>20.0272</td>\n",
       "      <td>0.2991</td>\n",
       "      <td>0.4169</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>61.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>19.8121</td>\n",
       "      <td>0.3935</td>\n",
       "      <td>0.5572</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>18.8667</td>\n",
       "      <td>0.2902</td>\n",
       "      <td>0.4844</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>18.8311</td>\n",
       "      <td>0.2657</td>\n",
       "      <td>0.4996</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>65.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>18.1467</td>\n",
       "      <td>0.2762</td>\n",
       "      <td>0.3892</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          NO2     SO2      CO      O3  Temperature\n",
       "2000  40.1056  1.2726  1.0665  0.0177         62.4\n",
       "2001  37.9475  2.4532  1.1439  0.0189         61.1\n",
       "2002  31.6259  2.3950  0.9490  0.0167         59.5\n",
       "2003  33.6259  2.0295  0.9590  0.0187         61.7\n",
       "2004  25.1765  2.6180  0.5942  0.0281         62.6\n",
       "2005  20.0479  3.7157  0.4351  0.0234         62.3\n",
       "2006  21.9860  1.9429  0.3837  0.0223         63.0\n",
       "2007  21.4902  1.8018  0.3841  0.0238         61.6\n",
       "2008  20.4043  0.9937  0.3770  0.0238         62.5\n",
       "2009  21.8176  1.0298  0.5203  0.0229         61.7\n",
       "2010  18.6410  0.4666  0.4491  0.0226         61.3\n",
       "2011  20.0272  0.2991  0.4169  0.0223         61.3\n",
       "2012  19.8121  0.3935  0.5572  0.0224         62.7\n",
       "2013  18.8667  0.2902  0.4844  0.0235         63.0\n",
       "2014  18.8311  0.2657  0.4996  0.0261         65.3\n",
       "2015  18.1467  0.2762  0.3892  0.0276         64.9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.set_index(\"Unnamed: 0\")\n",
    "data.index.name = None\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>NO2</th>\n",
       "      <th>SO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433771</td>\n",
       "      <td>0.955387</td>\n",
       "      <td>-0.747276</td>\n",
       "      <td>-0.440765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>0.433771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.347214</td>\n",
       "      <td>-0.256947</td>\n",
       "      <td>-0.415365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.955387</td>\n",
       "      <td>0.347214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.754538</td>\n",
       "      <td>-0.429522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>-0.747276</td>\n",
       "      <td>-0.256947</td>\n",
       "      <td>-0.754538</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.696165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>-0.440765</td>\n",
       "      <td>-0.415365</td>\n",
       "      <td>-0.429522</td>\n",
       "      <td>0.696165</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  NO2       SO2        CO        O3  Temperature\n",
       "NO2          1.000000  0.433771  0.955387 -0.747276    -0.440765\n",
       "SO2          0.433771  1.000000  0.347214 -0.256947    -0.415365\n",
       "CO           0.955387  0.347214  1.000000 -0.754538    -0.429522\n",
       "O3          -0.747276 -0.256947 -0.754538  1.000000     0.696165\n",
       "Temperature -0.440765 -0.415365 -0.429522  0.696165     1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>5. Checking relation between data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce6547ebb0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['NO2','SO2','CO','O3', 'Temperature']].hist(figsize=(12, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, figsize=(12,14))\n",
    "\n",
    "axs[0].scatter(data['Temperature'], data['NO2'])\n",
    "axs[0].set_title(\"Relation: Temperature X NO2\")\n",
    "axs[0].set_xlabel(\"Temperature\")\n",
    "axs[0].set_ylabel(\"NO2\")\n",
    "\n",
    "axs[1].scatter(data['Temperature'], data['SO2'])\n",
    "axs[1].set_title(\"Relation: Temperature X SO2\")\n",
    "axs[1].set_xlabel(\"Temperature\")\n",
    "axs[1].set_ylabel(\"SO2\")\n",
    "\n",
    "axs[2].scatter(data['Temperature'], data['CO'])\n",
    "axs[2].set_title(\"Relation: Temperature X CO\")\n",
    "axs[2].set_xlabel(\"Temperature\")\n",
    "axs[2].set_ylabel(\"CO\")\n",
    "\n",
    "axs[3].scatter(data['Temperature'], data['O3'])\n",
    "axs[3].set_title(\"Relation: Temperature X O3\")\n",
    "axs[3].set_xlabel(\"Temperature\")\n",
    "axs[3].set_ylabel(\"O3\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>6. Splitting the data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.01056e+01, 1.06650e+00, 1.27260e+00, 1.77000e-02],\n",
       "       [3.79475e+01, 1.14390e+00, 2.45320e+00, 1.89000e-02],\n",
       "       [3.16259e+01, 9.49000e-01, 2.39500e+00, 1.67000e-02],\n",
       "       [3.36259e+01, 9.59000e-01, 2.02950e+00, 1.87000e-02],\n",
       "       [2.51765e+01, 5.94200e-01, 2.61800e+00, 2.81000e-02],\n",
       "       [2.00479e+01, 4.35100e-01, 3.71570e+00, 2.34000e-02],\n",
       "       [2.19860e+01, 3.83700e-01, 1.94290e+00, 2.23000e-02],\n",
       "       [2.14902e+01, 3.84100e-01, 1.80180e+00, 2.38000e-02],\n",
       "       [2.04043e+01, 3.77000e-01, 9.93700e-01, 2.38000e-02],\n",
       "       [2.18176e+01, 5.20300e-01, 1.02980e+00, 2.29000e-02],\n",
       "       [1.86410e+01, 4.49100e-01, 4.66600e-01, 2.26000e-02],\n",
       "       [2.00272e+01, 4.16900e-01, 2.99100e-01, 2.23000e-02],\n",
       "       [1.98121e+01, 5.57200e-01, 3.93500e-01, 2.24000e-02],\n",
       "       [1.88667e+01, 4.84400e-01, 2.90200e-01, 2.35000e-02],\n",
       "       [1.88311e+01, 4.99600e-01, 2.65700e-01, 2.61000e-02],\n",
       "       [1.81467e+01, 3.89200e-01, 2.76200e-01, 2.76000e-02]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[['NO2', 'CO', 'SO2', 'O3']].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([62.4, 61.1, 59.5, 61.7, 62.6, 62.3, 63. , 61.6, 62.5, 61.7, 61.3,\n",
       "       61.3, 62.7, 63. , 65.3, 64.9])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['Temperature'].values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset using Scikit learn\n",
    "\n",
    "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=.10, random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 4)\n",
      "(2, 4)\n",
      "(14,)\n",
      "(2,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>7. Creating the Model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building your model using any of the algorithms, using Scikit learn library\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>8. Making Prediction</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([61.34301104, 59.72230704, 62.02273035, 62.06574137, 65.21449087,\n",
       "       61.77340047, 62.718755  , 61.97571522, 61.6894748 , 62.73447984,\n",
       "       64.44089858, 62.16434241, 61.94202724, 62.79262576])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict/Classify the model for test and training data sets\n",
    "\n",
    "y_pred = model.predict(X_train)   \n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([66.01362816, 62.51937985])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test)   \n",
    "y_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "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>ACTUAL</th>\n",
       "      <th>PREDICTED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.7</td>\n",
       "      <td>61.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59.5</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.3</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.1</td>\n",
       "      <td>62.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.9</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>63.0</td>\n",
       "      <td>61.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>63.0</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>61.3</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62.4</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61.6</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65.3</td>\n",
       "      <td>64.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62.7</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>62.3</td>\n",
       "      <td>61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>62.5</td>\n",
       "      <td>62.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ACTUAL  PREDICTED\n",
       "0     61.7       61.3\n",
       "1     59.5       59.7\n",
       "2     61.3       62.0\n",
       "3     61.1       62.1\n",
       "4     64.9       65.2\n",
       "5     63.0       61.8\n",
       "6     63.0       62.7\n",
       "7     61.3       62.0\n",
       "8     62.4       61.7\n",
       "9     61.6       62.7\n",
       "10    65.3       64.4\n",
       "11    62.7       62.2\n",
       "12    62.3       61.9\n",
       "13    62.5       62.8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model. Compare train and test and evaluate model.\n",
    "\n",
    "data_predicted = pd.DataFrame({'ACTUAL': y_train, 'PREDICTED': np.around(y_pred, decimals=1)})\n",
    "data_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTUAL</th>\n",
       "      <th>PREDICTED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.6</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.7</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACTUAL  PREDICTED\n",
       "0    62.6       66.0\n",
       "1    61.7       62.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_predicted_test = pd.DataFrame({'ACTUAL': y_test, 'PREDICTED': np.around(y_pred_test, decimals=1)})\n",
    "data_predicted_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>9. Showing Results</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7660137161885681"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provide comparative results, like Accuracy, Score. \n",
    "\n",
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-29.43022362090053"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_round = np.around(y_train, 0)\n",
    "y_pred_round = np.around(y_pred, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35714285714285715"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_train_round, y_pred_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.61240399e-01, -8.02908898e-01, -1.06935237e+00, -1.95418787e-02,\n",
       "       -6.92390580e+01])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores_train = cross_val_score(model, X_train, y_train)\n",
    "scores_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('NO2', 0.10805979738386873),\n",
       " ('CO', 1.196673422350195),\n",
       " ('SO2', -0.22767533884774382),\n",
       " ('O3', 654.7011673726919)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(['NO2', 'CO', 'SO2', 'O3'], model.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.78094856034956"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7660137161885681"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the Scikit Learn to call functions r2_score, Mean Squared Error for train and testdata set.\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r2_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4858319288770428"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(y_pred, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0, 0],\n",
       "       [0, 1, 2, 2, 0, 0],\n",
       "       [0, 0, 2, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Design and explain your Confusion matrix. Also infer if your model is overfitting or underfitting.\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_round, y_pred_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        60.0       1.00      1.00      1.00         1\n",
      "        61.0       0.00      0.00      0.00         3\n",
      "        62.0       0.29      0.40      0.33         5\n",
      "        63.0       0.33      0.33      0.33         3\n",
      "        64.0       0.00      0.00      0.00         0\n",
      "        65.0       1.00      0.50      0.67         2\n",
      "\n",
      "    accuracy                           0.36        14\n",
      "   macro avg       0.44      0.37      0.39        14\n",
      "weighted avg       0.39      0.36      0.36        14\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rmart\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_train_round, y_pred_round))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.1 64-bit",
   "language": "python",
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