Python - Linear Regression - Predicting Temperature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-04-01 00:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
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       "      <th>1</th>\n",
       "      <td>2006-04-01 01:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-04-01 02:00:00.000 +0200</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  Formatted Date        Summary Precip Type  Temperature (C)  \\\n",
       "0  2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "1  2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2  2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "\n",
       "   Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "0                  7.388889      0.89            14.1197   \n",
       "1                  7.227778      0.86            14.2646   \n",
       "2                  9.377778      0.89             3.9284   \n",
       "\n",
       "   Wind Bearing (degrees)  Visibility (km)  Loud Cover  Pressure (millibars)  \\\n",
       "0                   251.0          15.8263         0.0               1015.13   \n",
       "1                   259.0          15.8263         0.0               1015.63   \n",
       "2                   204.0          14.9569         0.0               1015.94   \n",
       "\n",
       "                       Daily Summary  \n",
       "0  Partly cloudy throughout the day.  \n",
       "1  Partly cloudy throughout the day.  \n",
       "2  Partly cloudy throughout the day.  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the data\n",
    "weather_data = pd.read_csv(\"D:\\Machine Learning - Full\\Blogs dataset\\weatherHist.csv\")\n",
    "weather_data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96453 entries, 0 to 96452\n",
      "Data columns (total 12 columns):\n",
      "Formatted Date              96453 non-null object\n",
      "Summary                     96453 non-null object\n",
      "Precip Type                 96453 non-null object\n",
      "Temperature (C)             96453 non-null float64\n",
      "Apparent Temperature (C)    96453 non-null float64\n",
      "Humidity                    96453 non-null float64\n",
      "Wind Speed (km/h)           96453 non-null float64\n",
      "Wind Bearing (degrees)      96453 non-null float64\n",
      "Visibility (km)             96453 non-null float64\n",
      "Loud Cover                  96453 non-null float64\n",
      "Pressure (millibars)        96453 non-null float64\n",
      "Daily Summary               96453 non-null object\n",
      "dtypes: float64(8), object(4)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "weather_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mostly cloudy throughout the day.</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>11.932678</td>\n",
       "      <td>10.855029</td>\n",
       "      <td>0.734899</td>\n",
       "      <td>10.810640</td>\n",
       "      <td>187.509232</td>\n",
       "      <td>10.347325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1003.235956</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.551546</td>\n",
       "      <td>10.696847</td>\n",
       "      <td>0.195473</td>\n",
       "      <td>6.913571</td>\n",
       "      <td>107.383428</td>\n",
       "      <td>4.192123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.969906</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>-21.822222</td>\n",
       "      <td>-27.716667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.688889</td>\n",
       "      <td>2.311111</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.828200</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>8.339800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.900000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>9.965900</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>10.046400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.450000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>18.838889</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>14.135800</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>14.812000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021.090000</td>\n",
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       "      <td>39.344444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.852600</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1046.380000</td>\n",
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      "text/plain": [
       "                       Formatted Date        Summary Precip Type  \\\n",
       "count                           96453          96453       96453   \n",
       "unique                          96429             27           3   \n",
       "top     2010-08-02 17:00:00.000 +0200  Partly Cloudy        rain   \n",
       "freq                                2          31733       85224   \n",
       "mean                              NaN            NaN         NaN   \n",
       "std                               NaN            NaN         NaN   \n",
       "min                               NaN            NaN         NaN   \n",
       "25%                               NaN            NaN         NaN   \n",
       "50%                               NaN            NaN         NaN   \n",
       "75%                               NaN            NaN         NaN   \n",
       "max                               NaN            NaN         NaN   \n",
       "\n",
       "        Temperature (C)  Apparent Temperature (C)      Humidity  \\\n",
       "count      96453.000000              96453.000000  96453.000000   \n",
       "unique              NaN                       NaN           NaN   \n",
       "top                 NaN                       NaN           NaN   \n",
       "freq                NaN                       NaN           NaN   \n",
       "mean          11.932678                 10.855029      0.734899   \n",
       "std            9.551546                 10.696847      0.195473   \n",
       "min          -21.822222                -27.716667      0.000000   \n",
       "25%            4.688889                  2.311111      0.600000   \n",
       "50%           12.000000                 12.000000      0.780000   \n",
       "75%           18.838889                 18.838889      0.890000   \n",
       "max           39.905556                 39.344444      1.000000   \n",
       "\n",
       "        Wind Speed (km/h)  Wind Bearing (degrees)  Visibility (km)  \\\n",
       "count        96453.000000            96453.000000     96453.000000   \n",
       "unique                NaN                     NaN              NaN   \n",
       "top                   NaN                     NaN              NaN   \n",
       "freq                  NaN                     NaN              NaN   \n",
       "mean            10.810640              187.509232        10.347325   \n",
       "std              6.913571              107.383428         4.192123   \n",
       "min              0.000000                0.000000         0.000000   \n",
       "25%              5.828200              116.000000         8.339800   \n",
       "50%              9.965900              180.000000        10.046400   \n",
       "75%             14.135800              290.000000        14.812000   \n",
       "max             63.852600              359.000000        16.100000   \n",
       "\n",
       "        Loud Cover  Pressure (millibars)                      Daily Summary  \n",
       "count      96453.0          96453.000000                              96453  \n",
       "unique         NaN                   NaN                                214  \n",
       "top            NaN                   NaN  Mostly cloudy throughout the day.  \n",
       "freq           NaN                   NaN                              20085  \n",
       "mean           0.0           1003.235956                                NaN  \n",
       "std            0.0            116.969906                                NaN  \n",
       "min            0.0              0.000000                                NaN  \n",
       "25%            0.0           1011.900000                                NaN  \n",
       "50%            0.0           1016.450000                                NaN  \n",
       "75%            0.0           1021.090000                                NaN  \n",
       "max            0.0           1046.380000                                NaN  "
      ]
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     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# describes allnumerical and  categorical data\n",
    "weather_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
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       "      <th>top</th>\n",
       "      <td>2010-08-02 17:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
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      "text/plain": [
       "                       Formatted Date        Summary Precip Type  \\\n",
       "count                           96453          96453       96453   \n",
       "unique                          96429             27           3   \n",
       "top     2010-08-02 17:00:00.000 +0200  Partly Cloudy        rain   \n",
       "freq                                2          31733       85224   \n",
       "\n",
       "                            Daily Summary  \n",
       "count                               96453  \n",
       "unique                                214  \n",
       "top     Mostly cloudy throughout the day.  \n",
       "freq                                20085  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describes all  categorical data\n",
    "weather_data.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature (C)</th>\n",
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       "      <th>Temperature (C)</th>\n",
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       "      <td>0.392847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <td>0.992629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.602571</td>\n",
       "      <td>-0.056650</td>\n",
       "      <td>0.029031</td>\n",
       "      <td>0.381718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity</th>\n",
       "      <td>-0.632255</td>\n",
       "      <td>-0.602571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.224951</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>-0.369173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <td>0.008957</td>\n",
       "      <td>-0.056650</td>\n",
       "      <td>-0.224951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103822</td>\n",
       "      <td>0.100749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <td>0.029988</td>\n",
       "      <td>0.029031</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.103822</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visibility (km)</th>\n",
       "      <td>0.392847</td>\n",
       "      <td>0.381718</td>\n",
       "      <td>-0.369173</td>\n",
       "      <td>0.100749</td>\n",
       "      <td>0.047594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.059818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loud Cover</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <td>-0.005447</td>\n",
       "      <td>-0.000219</td>\n",
       "      <td>0.005454</td>\n",
       "      <td>-0.049263</td>\n",
       "      <td>-0.011651</td>\n",
       "      <td>0.059818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Temperature (C)  Apparent Temperature (C)  Humidity  \\\n",
       "Temperature (C)                  1.000000                  0.992629 -0.632255   \n",
       "Apparent Temperature (C)         0.992629                  1.000000 -0.602571   \n",
       "Humidity                        -0.632255                 -0.602571  1.000000   \n",
       "Wind Speed (km/h)                0.008957                 -0.056650 -0.224951   \n",
       "Wind Bearing (degrees)           0.029988                  0.029031  0.000735   \n",
       "Visibility (km)                  0.392847                  0.381718 -0.369173   \n",
       "Loud Cover                            NaN                       NaN       NaN   \n",
       "Pressure (millibars)            -0.005447                 -0.000219  0.005454   \n",
       "\n",
       "                          Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",
       "Temperature (C)                    0.008957                0.029988   \n",
       "Apparent Temperature (C)          -0.056650                0.029031   \n",
       "Humidity                          -0.224951                0.000735   \n",
       "Wind Speed (km/h)                  1.000000                0.103822   \n",
       "Wind Bearing (degrees)             0.103822                1.000000   \n",
       "Visibility (km)                    0.100749                0.047594   \n",
       "Loud Cover                              NaN                     NaN   \n",
       "Pressure (millibars)              -0.049263               -0.011651   \n",
       "\n",
       "                          Visibility (km)  Loud Cover  Pressure (millibars)  \n",
       "Temperature (C)                  0.392847         NaN             -0.005447  \n",
       "Apparent Temperature (C)         0.381718         NaN             -0.000219  \n",
       "Humidity                        -0.369173         NaN              0.005454  \n",
       "Wind Speed (km/h)                0.100749         NaN             -0.049263  \n",
       "Wind Bearing (degrees)           0.047594         NaN             -0.011651  \n",
       "Visibility (km)                  1.000000         NaN              0.059818  \n",
       "Loud Cover                            NaN         NaN                   NaN  \n",
       "Pressure (millibars)             0.059818         NaN              1.000000  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Visibility (km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature (C)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992629</td>\n",
       "      <td>-0.632255</td>\n",
       "      <td>0.392847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <td>0.992629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.602571</td>\n",
       "      <td>0.381718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity</th>\n",
       "      <td>-0.632255</td>\n",
       "      <td>-0.602571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Visibility (km)</th>\n",
       "      <td>0.392847</td>\n",
       "      <td>0.381718</td>\n",
       "      <td>-0.369173</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Temperature (C)  Apparent Temperature (C)  Humidity  \\\n",
       "Temperature (C)                  1.000000                  0.992629 -0.632255   \n",
       "Apparent Temperature (C)         0.992629                  1.000000 -0.602571   \n",
       "Humidity                        -0.632255                 -0.602571  1.000000   \n",
       "Visibility (km)                  0.392847                  0.381718 -0.369173   \n",
       "\n",
       "                          Visibility (km)  \n",
       "Temperature (C)                  0.392847  \n",
       "Apparent Temperature (C)         0.381718  \n",
       "Humidity                        -0.369173  \n",
       "Visibility (km)                  1.000000  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set=weather_data.iloc[:,[0,3,4,5,8]]\n",
    "data_set.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c94cd55978>"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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xJxnaed+2deOignYeOM1rnedwcEPiRsOWFHETqhrbcTANI5kVLApgdhEFkCfrV7UyNBKN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\nCkAQBKFKEQUgCIJQpfwfGq93ROP6t04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca16fdf9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a scatter plot between temp and humdity\n",
    "sns.regplot(x=data_set[\"Temperature (C)\"], y=data_set[\"Humidity\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a negative correlationship between Humidity and Temperature and we also see a few outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "outliers=[]\n",
    "def detect_outlier(data_1):\n",
    "    \n",
    "    threshold=3\n",
    "    mean_1 = np.mean(data_1)\n",
    "    std_1 =np.std(data_1)\n",
    "    \n",
    "    \n",
    "    for y in data_1:\n",
    "        z_score= (y - mean_1)/std_1 \n",
    "        if np.abs(z_score) > threshold:\n",
    "            outliers.append(y)\n",
    "    return outliers\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14000000000000001, 0.14000000000000001, 0.14000000000000001, 0.13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12, 0.14000000000000001]\n"
     ]
    }
   ],
   "source": [
    "outlier_data = detect_outlier(data_set[\"Humidity\"])\n",
    "print (outlier_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set_clean = data_set[data_set[\"Humidity\"]>0.15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ca176f4ef0>"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jGjg4OHb+/WB+r0lrcyD9ePlCdzfkqbMjRGNiCYS5R+b2mFjc5oEnjzC/pYF5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\n4PG4WeaUNMfVFy8kbtkcPTWcHm8i4RS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c940788438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a scatter plot between temp and humdity after removing outliers\n",
    "sns.regplot(x=data_set_clean[\"Temperature (C)\"], y=data_set_clean[\"Humidity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ca17603860>"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fwd/ufYUl72y/HXNvH937TOi+OwiCcHno6bdZl+MdzMbUDGFIRZyxNUOYtv84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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c94cd049b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot between Temp and Apparent temp\n",
    "sns.regplot(x=data_set[\"Temperature (C)\"], y=data_set[\"Apparent Temperature (C)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a strong positive correlation between temp and apparent temperatue\n",
    "this seems obvious too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ca1785aac8>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ri1NJLRKXCkNC8Qk9rT6MlaqvGBga/JBDB7I88PCpqFY/3jBTrTl4noeuqTiBdok0+JcW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\nviAIQhchRl8QBKGLEKMvCILQRfw/A++a2KzHE9MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca177820f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot between Temp and Visibilty\n",
    "sns.regplot(x=data_set[\"Temperature (C)\"], y=data_set[\"Visibility (km)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we don't see any strong relationship so we can drop visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ca1767e550>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DqKprwX+W7jG9PgD4bPk+S+qJ5vF310fMZ5fLOicIO5UkR76yrV4hTh7Zo1Mr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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ca1769cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a time series for temperature\n",
    "sns.tsplot(data_set[\"Temperature (C)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c94d82cbe0>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tIJxY2Zlu1o186ox7ISgjc+SRhPfsCjI6fZjZrRqMPCjCbxWPx8O4+gpHlIMv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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c94d6b0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.tsplot(data_set[\"Humidity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Formatted Date</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Visibility (km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-04-01 00:00:00.000 +0200</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>15.8263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Formatted Date  Temperature (C)  Apparent Temperature (C)  \\\n",
       "0  2006-04-01 00:00:00.000 +0200         9.472222                  7.388889   \n",
       "\n",
       "   Humidity  Visibility (km)  \n",
       "0      0.89          15.8263  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set_clean = data_set[data_set[\"Humidity\"]>0.15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Formatted Date, Temperature (C), Apparent Temperature (C), Humidity, Visibility (km)]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "print(data_set_clean[data_set_clean[\"Humidity\"]<=0.15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating independent and dependent variables\n",
    "\n",
    "y= data_set_clean.iloc[:,[1]]\n",
    "X= data_set_clean.iloc[:,[2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Apparent Temperature (C)  Humidity\n",
       "0                  7.388889      0.89"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set in to 80:20 ratio\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting Multiple Linear regression to Training set\n",
    "from sklearn.linear_model import LinearRegression\n",
    "regressor =LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.85716489, -2.64871695]])"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.57532511])"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting test set result\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98716959300861884"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returns the coefficient of determination\n",
    "regressor.score(X,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0869884738697586\n"
     ]
    }
   ],
   "source": [
    "# RMSE\n",
    "from sklearn import metrics\n",
    "import math\n",
    "print(math.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "ones_1 =[1] * X.count()\n",
    "X[\"b0\"]=ones_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>3.454e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 22 Sep 2018</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:23:46</td>     <th>  Log-Likelihood:    </th> <td> -23098.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 19283</td>      <th>  AIC:               </th> <td>4.620e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 19281</td>      <th>  BIC:               </th> <td>4.622e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Apparent Temperature (C)</th> <td>    0.9262</td> <td>    0.000</td> <td> 1987.868</td> <td> 0.000</td> <td>    0.925</td> <td>    0.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Humidity</th>                 <td>    2.3635</td> <td>    0.009</td> <td>  254.432</td> <td> 0.000</td> <td>    2.345</td> <td>    2.382</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>211.488</td> <th>  Durbin-Watson:     </th> <td>   1.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 211.350</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.240</td>  <th>  Prob(JB):          </th> <td>1.28e-46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.821</td>  <th>  Cond. No.          </th> <td>    24.5</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.997\n",
       "Model:                            OLS   Adj. R-squared:                  0.997\n",
       "Method:                 Least Squares   F-statistic:                 3.454e+06\n",
       "Date:                Sat, 22 Sep 2018   Prob (F-statistic):               0.00\n",
       "Time:                        17:23:46   Log-Likelihood:                -23098.\n",
       "No. Observations:               19283   AIC:                         4.620e+04\n",
       "Df Residuals:                   19281   BIC:                         4.622e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================\n",
       "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------\n",
       "Apparent Temperature (C)     0.9262      0.000   1987.868      0.000       0.925       0.927\n",
       "Humidity                     2.3635      0.009    254.432      0.000       2.345       2.382\n",
       "==============================================================================\n",
       "Omnibus:                      211.488   Durbin-Watson:                   1.973\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              211.350\n",
       "Skew:                           0.240   Prob(JB):                     1.28e-46\n",
       "Kurtosis:                       2.821   Cond. No.                         24.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(y_pred,X_test).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0869884738697586\n"
     ]
    }
   ],
   "source": [
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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