regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(145460, 23)\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>Albury</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>44.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>Albury</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>Albury</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WSW</td>\n",
       "      <td>46.0</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>Albury</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>Albury</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>41.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   \n",
       "1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   \n",
       "2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   \n",
       "3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   \n",
       "4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   \n",
       "\n",
       "  WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \\\n",
       "0           W           44.0          W  ...        71.0         22.0   \n",
       "1         WNW           44.0        NNW  ...        44.0         25.0   \n",
       "2         WSW           46.0          W  ...        38.0         30.0   \n",
       "3          NE           24.0         SE  ...        45.0         16.0   \n",
       "4           W           41.0        ENE  ...        82.0         33.0   \n",
       "\n",
       "   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \\\n",
       "0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   \n",
       "1       1010.6       1007.8       NaN       NaN     17.2     24.3         No   \n",
       "2       1007.6       1008.7       NaN       2.0     21.0     23.2         No   \n",
       "3       1017.6       1012.8       NaN       NaN     18.1     26.5         No   \n",
       "4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   \n",
       "\n",
       "   RainTomorrow  \n",
       "0            No  \n",
       "1            No  \n",
       "2            No  \n",
       "3            No  \n",
       "4            No  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#carregando os dados\n",
    "df=pd.read_csv(\"data/weatherAUS.csv\")\n",
    "#Exibindo a quantidade de linhas e de colunas que o dataset tem (nesse caso é 145460 linhas e 23 colunas)\n",
    "print(df.shape)\n",
    "#olhando as 5 primeiras linhas do dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>143975.00</td>\n",
       "      <td>144199.00</td>\n",
       "      <td>142199.00</td>\n",
       "      <td>82670.00</td>\n",
       "      <td>75625.00</td>\n",
       "      <td>135197.00</td>\n",
       "      <td>143693.00</td>\n",
       "      <td>142398.00</td>\n",
       "      <td>142806.00</td>\n",
       "      <td>140953.00</td>\n",
       "      <td>130395.00</td>\n",
       "      <td>130432.00</td>\n",
       "      <td>89572.00</td>\n",
       "      <td>86102.00</td>\n",
       "      <td>143693.00</td>\n",
       "      <td>141851.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.19</td>\n",
       "      <td>23.22</td>\n",
       "      <td>2.36</td>\n",
       "      <td>5.47</td>\n",
       "      <td>7.61</td>\n",
       "      <td>40.04</td>\n",
       "      <td>14.04</td>\n",
       "      <td>18.66</td>\n",
       "      <td>68.88</td>\n",
       "      <td>51.54</td>\n",
       "      <td>1017.65</td>\n",
       "      <td>1015.26</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.51</td>\n",
       "      <td>16.99</td>\n",
       "      <td>21.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.40</td>\n",
       "      <td>7.12</td>\n",
       "      <td>8.48</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.79</td>\n",
       "      <td>13.61</td>\n",
       "      <td>8.92</td>\n",
       "      <td>8.81</td>\n",
       "      <td>19.03</td>\n",
       "      <td>20.80</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.04</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.72</td>\n",
       "      <td>6.49</td>\n",
       "      <td>6.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.50</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>980.50</td>\n",
       "      <td>977.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.20</td>\n",
       "      <td>-5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.60</td>\n",
       "      <td>17.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>4.80</td>\n",
       "      <td>31.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1012.90</td>\n",
       "      <td>1010.40</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.00</td>\n",
       "      <td>22.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.80</td>\n",
       "      <td>8.40</td>\n",
       "      <td>39.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>1017.60</td>\n",
       "      <td>1015.20</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>16.70</td>\n",
       "      <td>21.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.90</td>\n",
       "      <td>28.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>7.40</td>\n",
       "      <td>10.60</td>\n",
       "      <td>48.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>83.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>1022.40</td>\n",
       "      <td>1020.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>21.60</td>\n",
       "      <td>26.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.90</td>\n",
       "      <td>48.10</td>\n",
       "      <td>371.00</td>\n",
       "      <td>145.00</td>\n",
       "      <td>14.50</td>\n",
       "      <td>135.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1041.00</td>\n",
       "      <td>1039.60</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>40.20</td>\n",
       "      <td>46.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MinTemp    MaxTemp   Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
       "count  143975.00  144199.00  142199.00     82670.00  75625.00      135197.00   \n",
       "mean       12.19      23.22       2.36         5.47      7.61          40.04   \n",
       "std         6.40       7.12       8.48         4.19      3.79          13.61   \n",
       "min        -8.50      -4.80       0.00         0.00      0.00           6.00   \n",
       "25%         7.60      17.90       0.00         2.60      4.80          31.00   \n",
       "50%        12.00      22.60       0.00         4.80      8.40          39.00   \n",
       "75%        16.90      28.20       0.80         7.40     10.60          48.00   \n",
       "max        33.90      48.10     371.00       145.00     14.50         135.00   \n",
       "\n",
       "       WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
       "count     143693.00     142398.00    142806.00    140953.00    130395.00   \n",
       "mean          14.04         18.66        68.88        51.54      1017.65   \n",
       "std            8.92          8.81        19.03        20.80         7.11   \n",
       "min            0.00          0.00         0.00         0.00       980.50   \n",
       "25%            7.00         13.00        57.00        37.00      1012.90   \n",
       "50%           13.00         19.00        70.00        52.00      1017.60   \n",
       "75%           19.00         24.00        83.00        66.00      1022.40   \n",
       "max          130.00         87.00       100.00       100.00      1041.00   \n",
       "\n",
       "       Pressure3pm  Cloud9am  Cloud3pm    Temp9am    Temp3pm  \n",
       "count    130432.00  89572.00  86102.00  143693.00  141851.00  \n",
       "mean       1015.26      4.45      4.51      16.99      21.68  \n",
       "std           7.04      2.89      2.72       6.49       6.94  \n",
       "min         977.10      0.00      0.00      -7.20      -5.40  \n",
       "25%        1010.40      1.00      2.00      12.30      16.60  \n",
       "50%        1015.20      5.00      5.00      16.70      21.10  \n",
       "75%        1020.00      7.00      7.00      21.60      26.40  \n",
       "max        1039.60      9.00      9.00      40.20      46.70  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Usando a função describe que nos da uma visão estatistica geral do dataset e usando o round(2) para as saidas ficarem com 2 casas decimais\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "Location          object\n",
       "MinTemp          float64\n",
       "MaxTemp          float64\n",
       "Rainfall         float64\n",
       "Evaporation      float64\n",
       "Sunshine         float64\n",
       "WindGustDir       object\n",
       "WindGustSpeed    float64\n",
       "WindDir9am        object\n",
       "WindDir3pm        object\n",
       "WindSpeed9am     float64\n",
       "WindSpeed3pm     float64\n",
       "Humidity9am      float64\n",
       "Humidity3pm      float64\n",
       "Pressure9am      float64\n",
       "Pressure3pm      float64\n",
       "Cloud9am         float64\n",
       "Cloud3pm         float64\n",
       "Temp9am          float64\n",
       "Temp3pm          float64\n",
       "RainToday         object\n",
       "RainTomorrow      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Olhando o tipo de dados que cada coluna guarda\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date                 0\n",
      "Location             0\n",
      "MinTemp           1485\n",
      "MaxTemp           1261\n",
      "Rainfall          3261\n",
      "Evaporation      62790\n",
      "Sunshine         69835\n",
      "WindGustDir      10326\n",
      "WindGustSpeed    10263\n",
      "WindDir9am       10566\n",
      "WindDir3pm        4228\n",
      "WindSpeed9am      1767\n",
      "WindSpeed3pm      3062\n",
      "Humidity9am       2654\n",
      "Humidity3pm       4507\n",
      "Pressure9am      15065\n",
      "Pressure3pm      15028\n",
      "Cloud9am         55888\n",
      "Cloud3pm         59358\n",
      "Temp9am           1767\n",
      "Temp3pm           3609\n",
      "RainToday         3261\n",
      "RainTomorrow      3267\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Contando a quantidade de valores não preenchidos em cada coluna\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Evaporation      1.000000\n",
       "MaxTemp          0.587932\n",
       "Temp3pm          0.572893\n",
       "Temp9am          0.545115\n",
       "MinTemp          0.466993\n",
       "Sunshine         0.365602\n",
       "WindGustSpeed    0.203021\n",
       "WindSpeed9am     0.193084\n",
       "WindSpeed3pm     0.129400\n",
       "Rainfall        -0.064351\n",
       "Cloud3pm        -0.182618\n",
       "Cloud9am        -0.183793\n",
       "Pressure9am     -0.270362\n",
       "Pressure3pm     -0.293581\n",
       "Humidity3pm     -0.390243\n",
       "Humidity9am     -0.504092\n",
       "Name: Evaporation, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Olhando a correlação da coluna \"Evaporation\" em relação a outras colunas\n",
    "corr_matrix=df.corr()\n",
    "corr_matrix['Evaporation'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MaxTemp          1.000000\n",
       "Temp3pm          0.984503\n",
       "Temp9am          0.887210\n",
       "MinTemp          0.736555\n",
       "Evaporation      0.587932\n",
       "Sunshine         0.470156\n",
       "WindGustSpeed    0.067615\n",
       "WindSpeed3pm     0.050300\n",
       "WindSpeed9am     0.014450\n",
       "Rainfall        -0.074992\n",
       "Cloud3pm        -0.277921\n",
       "Cloud9am        -0.289370\n",
       "Pressure9am     -0.332061\n",
       "Pressure3pm     -0.427167\n",
       "Humidity9am     -0.504110\n",
       "Humidity3pm     -0.508855\n",
       "Name: MaxTemp, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Olhando a correlação da coluna \"MaxTemp\" em relação a outras colunas\n",
    "corr_matrix=df.corr()\n",
    "corr_matrix['MaxTemp'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fazendo um grafico para analisar se chover hoje tem impacto em chover amanhã\n",
    "sns.catplot(x=\"RainToday\", y=\"Temp3pm\", col='RainTomorrow', data=df, kind = \"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date                 0\n",
      "Location             0\n",
      "MaxTemp              0\n",
      "Temp9am              0\n",
      "Evaporation          0\n",
      "Humidity9am          0\n",
      "MinTemp             68\n",
      "Pressure9am        334\n",
      "WindSpeed9am       364\n",
      "Pressure3pm        367\n",
      "RainToday          390\n",
      "Rainfall           390\n",
      "RainTomorrow       733\n",
      "WindSpeed3pm       788\n",
      "Temp3pm           1125\n",
      "WindDir3pm        1142\n",
      "Humidity3pm       1251\n",
      "WindDir9am        3411\n",
      "WindGustSpeed     4838\n",
      "WindGustDir       4865\n",
      "Cloud9am          9561\n",
      "Sunshine         11071\n",
      "Cloud3pm         12177\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Retirando as linhas que tem 'MaxTemp', 'Humidity9am' ou 'Evaporation' com valores não preenchidos\n",
    "df_tmp = df.dropna(subset=['MaxTemp', 'Humidity9am', 'Evaporation'])\n",
    "print(df_tmp.isna().sum().sort_values(ascending=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separando as variaveis preditoras( \"MaxTemp\" e \"Humidity9am\" ) como X\n",
    "#E a varaivel de resposta (\"Evaporation\") como targer\n",
    "x = df_tmp[[\"MaxTemp\", \"Humidity9am\"]].to_numpy()\n",
    "target = df_tmp.Evaporation.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cria a regressão linear\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Treina a regressão linear\n",
    "lr.fit(x, target)\n",
    "#Faz a predição com a regressão linear gerada\n",
    "y = lr.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1df6eb6baf0>"
      ]
     },
     "execution_count": 13,
     "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": [
    "#Exibe o grafico dos resultados\n",
    "plt.hist(y, histtype='step', label='prediction')\n",
    "plt.hist(target, histtype='step', label='target')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 do modelo linear eh 10.339\n"
     ]
    }
   ],
   "source": [
    "#Avalia o erro do modelo\n",
    "r2=  mean_squared_error(y, target)\n",
    "print('R^2 do modelo linear eh {:.3f}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wo=[ 0.26645297 -0.06223077] y W1=3.265326298568664\n"
     ]
    }
   ],
   "source": [
    "#Exibe os pesos do modelo\n",
    "print(\"Wo={} y W1={}\".format(lr.coef_,lr.intercept_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date                 0\n",
      "Location             0\n",
      "MaxTemp              0\n",
      "Temp3pm              0\n",
      "MinTemp            391\n",
      "Temp9am            595\n",
      "WindSpeed3pm       804\n",
      "Humidity3pm        893\n",
      "WindSpeed9am      1200\n",
      "Humidity9am       1456\n",
      "WindDir3pm        1950\n",
      "RainTomorrow      2264\n",
      "RainToday         2361\n",
      "Rainfall          2361\n",
      "WindGustSpeed     7975\n",
      "WindGustDir       8037\n",
      "WindDir9am        9563\n",
      "Pressure3pm      12805\n",
      "Pressure9am      12985\n",
      "Cloud9am         54398\n",
      "Cloud3pm         55680\n",
      "Evaporation      60726\n",
      "Sunshine         66891\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Retirando as linhas que tem 'MaxTemp' ou 'Temp3pm' com valores não preenchidos\n",
    "df_tmp2 = df.dropna(subset=['MaxTemp', 'Temp3pm'])\n",
    "print(df_tmp2.isna().sum().sort_values(ascending=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separando a variavel preditora (temp3pm) na variavel x2\n",
    "x2 = df_tmp2.Temp3pm.to_numpy()\n",
    "#mudando a forma do array (quando se tem apenas 1 variavel preditora precisa fazer esse reshape para treinar o modelo)\n",
    "x2 = x2.reshape([-1,1])\n",
    "#Separando a variavel resposta (MaxTemp) na variavel t2\n",
    "t2 = df_tmp2.MaxTemp.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando um segundo modelo de regressão linear\n",
    "lr2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Treinando o segundo modelo\n",
    "lr2.fit(x2, t2)\n",
    "#Fazendo as predições com o novo modelo\n",
    "y2 = lr2.predict(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1df6ebd8670>"
      ]
     },
     "execution_count": 20,
     "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": [
    "#Criando os graficos\n",
    "plt.hist(y2, histtype='step', label='prediction')\n",
    "plt.hist(t2, histtype='step', label='target')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 do modelo linear eh 1.540\n"
     ]
    }
   ],
   "source": [
    "#Avaliando o erro do modelo\n",
    "r2=  mean_squared_error(y2, t2)\n",
    "print('R^2 do modelo linear eh {:.3f}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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