ML_Rainfall_Prediction_Update.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import The Libraries, Load The Dataset & Wrangle it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "import warnings\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "%matplotlib inline\n",
    "\n",
    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)\n",
    "\n",
    "# Load the clean version of the dataset\n",
    "# Assign to a dataframe\n",
    "# Perform feature selection:\n",
    "df_rainfall = pd.read_csv(\"austin_weather_clean.csv\")\n",
    "df_rainfall.drop(columns = [\"DewPointHighF\", \"DewPointLowF\"], inplace = True)\n",
    "\n",
    "# Remove outliers from the data;\n",
    "#'VisibilityHighMiles' column:\n",
    "low, high = df_rainfall[\"VisibilityHighMiles\"].quantile([0.01, 1])\n",
    "mask_visibilityH = df_rainfall[\"VisibilityHighMiles\"].between(low, high)\n",
    "#'SeaLevelPressureAvgInches' column:\n",
    "low, high = df_rainfall[\"SeaLevelPressureAvgInches\"].quantile([0.25, 1])\n",
    "mask_seaLevel = df_rainfall[\"SeaLevelPressureAvgInches\"].between(low, high)\n",
    "\n",
    "# Transform the dataframe:\n",
    "df_rainfall = df_rainfall[mask_visibilityH & mask_seaLevel]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract The Independent & Dependent Variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The independent variables:\n",
    "input_ds = df_rainfall.drop(columns = [\"PrecipitationSumInches\", \"Unnamed: 0\"])\n",
    "# The dependent variable:\n",
    "output_ds = df_rainfall[\"PrecipitationSumInches\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TempHighF</th>\n",
       "      <th>TempAvgF</th>\n",
       "      <th>TempLowF</th>\n",
       "      <th>DewPointAvgF</th>\n",
       "      <th>HumidityHighPercent</th>\n",
       "      <th>HumidityAvgPercent</th>\n",
       "      <th>HumidityLowPercent</th>\n",
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       "      <th>VisibilityHighMiles</th>\n",
       "      <th>VisibilityAvgMiles</th>\n",
       "      <th>VisibilityLowMiles</th>\n",
       "      <th>WindHighMPH</th>\n",
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       "      <th>WindGustMPH</th>\n",
       "      <th>PrecipitationSumInches</th>\n",
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       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>58</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>27.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.49</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.45</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>58</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>40.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>57</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>30.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>39.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>30.39</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>39.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>30.17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>8</th>\n",
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       "      <td>64</td>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>41.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30.10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
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       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>26.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>30.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>28.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.39</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "    Unnamed: 0  TempHighF  TempAvgF  TempLowF  DewPointAvgF  \\\n",
       "1            1         56        48        39          36.0   \n",
       "2            2         58        45        32          27.0   \n",
       "3            3         61        46        31          28.0   \n",
       "4            4         58        50        41          40.0   \n",
       "5            5         57        48        39          36.0   \n",
       "6            6         60        53        45          39.0   \n",
       "7            7         62        51        40          39.0   \n",
       "8            8         64        50        36          41.0   \n",
       "9            9         44        40        35          26.0   \n",
       "10          10         55        46        36          28.0   \n",
       "\n",
       "    HumidityHighPercent  HumidityAvgPercent  HumidityLowPercent  \\\n",
       "1                  93.0                68.0                43.0   \n",
       "2                  76.0                52.0                27.0   \n",
       "3                  89.0                56.0                22.0   \n",
       "4                  86.0                71.0                56.0   \n",
       "5                  79.0                63.0                47.0   \n",
       "6                  83.0                65.0                47.0   \n",
       "7                  92.0                64.0                36.0   \n",
       "8                  92.0                76.0                60.0   \n",
       "9                  75.0                60.0                45.0   \n",
       "10                 76.0                54.0                32.0   \n",
       "\n",
       "    SeaLevelPressureAvgInches  VisibilityHighMiles  VisibilityAvgMiles  \\\n",
       "1                       30.13                 10.0                10.0   \n",
       "2                       30.49                 10.0                10.0   \n",
       "3                       30.45                 10.0                10.0   \n",
       "4                       30.33                 10.0                10.0   \n",
       "5                       30.40                 10.0                 9.0   \n",
       "6                       30.39                 10.0                 9.0   \n",
       "7                       30.17                 10.0                10.0   \n",
       "8                       30.10                 10.0                10.0   \n",
       "9                       30.33                 10.0                10.0   \n",
       "10                      30.39                 10.0                10.0   \n",
       "\n",
       "    VisibilityLowMiles  WindHighMPH  WindAvgMPH  WindGustMPH  \\\n",
       "1                  5.0         16.0         6.0         25.0   \n",
       "2                 10.0          8.0         3.0         12.0   \n",
       "3                  7.0         12.0         4.0         20.0   \n",
       "4                  7.0         10.0         2.0         16.0   \n",
       "5                  7.0         12.0         3.0         17.0   \n",
       "6                  7.0          7.0         1.0         11.0   \n",
       "7                  7.0         10.0         2.0         14.0   \n",
       "8                  4.0         17.0         5.0         24.0   \n",
       "9                 10.0         13.0         5.0         21.0   \n",
       "10                10.0          8.0         1.0         12.0   \n",
       "\n",
       "    PrecipitationSumInches  \n",
       "1                      0.0  \n",
       "2                      0.0  \n",
       "3                      0.0  \n",
       "4                      0.0  \n",
       "5                      0.0  \n",
       "6                      0.0  \n",
       "7                      0.0  \n",
       "8                      0.0  \n",
       "9                      0.0  \n",
       "10                     0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the dataframe; the first 10 rows\n",
    "df_rainfall.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>TempHighF</th>\n",
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       "      <th>VisibilityLowMiles</th>\n",
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       "      <td>76</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>20.0</td>\n",
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       "      <th>1306</th>\n",
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       "      <td>77</td>\n",
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       "      <td>88.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.07</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>1307</th>\n",
       "      <td>1307</td>\n",
       "      <td>103</td>\n",
       "      <td>90</td>\n",
       "      <td>77</td>\n",
       "      <td>69.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1308</td>\n",
       "      <td>104</td>\n",
       "      <td>91</td>\n",
       "      <td>77</td>\n",
       "      <td>68.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.95</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>1311</td>\n",
       "      <td>102</td>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "      <td>71.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.95</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.01</td>\n",
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       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>1312</td>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>71.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>1313</td>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>70.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>1314</td>\n",
       "      <td>103</td>\n",
       "      <td>89</td>\n",
       "      <td>75</td>\n",
       "      <td>67.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>1317</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>79</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.91</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>1318</td>\n",
       "      <td>99</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>61.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  TempHighF  TempAvgF  TempLowF  DewPointAvgF  \\\n",
       "1305        1305        100        88        76          70.0   \n",
       "1306        1306        101        89        77          70.0   \n",
       "1307        1307        103        90        77          69.0   \n",
       "1308        1308        104        91        77          68.0   \n",
       "1311        1311        102        89        76          71.0   \n",
       "1312        1312        103        91        78          71.0   \n",
       "1313        1313        103        91        78          70.0   \n",
       "1314        1314        103        89        75          67.0   \n",
       "1317        1317        106        93        79          68.0   \n",
       "1318        1318         99        88        77          61.0   \n",
       "\n",
       "      HumidityHighPercent  HumidityAvgPercent  HumidityLowPercent  \\\n",
       "1305                 88.0                61.0                33.0   \n",
       "1306                 88.0                60.0                31.0   \n",
       "1307                 85.0                56.0                27.0   \n",
       "1308                 85.0                56.0                27.0   \n",
       "1311                 91.0                60.0                29.0   \n",
       "1312                 91.0                61.0                31.0   \n",
       "1313                 91.0                58.0                25.0   \n",
       "1314                 82.0                54.0                25.0   \n",
       "1317                 69.0                48.0                27.0   \n",
       "1318                 64.0                43.0                22.0   \n",
       "\n",
       "      SeaLevelPressureAvgInches  VisibilityHighMiles  VisibilityAvgMiles  \\\n",
       "1305                      29.98                 10.0                10.0   \n",
       "1306                      30.07                 10.0                10.0   \n",
       "1307                      30.03                 10.0                10.0   \n",
       "1308                      29.95                 10.0                10.0   \n",
       "1311                      29.95                 10.0                10.0   \n",
       "1312                      30.03                 10.0                10.0   \n",
       "1313                      30.03                 10.0                10.0   \n",
       "1314                      29.97                 10.0                10.0   \n",
       "1317                      29.91                 10.0                10.0   \n",
       "1318                      29.97                 10.0                10.0   \n",
       "\n",
       "      VisibilityLowMiles  WindHighMPH  WindAvgMPH  WindGustMPH  \\\n",
       "1305                10.0         14.0         5.0         20.0   \n",
       "1306                10.0         18.0         5.0         29.0   \n",
       "1307                10.0         14.0         5.0         22.0   \n",
       "1308                10.0         14.0         5.0         24.0   \n",
       "1311                 9.0         13.0         3.0         19.0   \n",
       "1312                 9.0         15.0         7.0         24.0   \n",
       "1313                10.0         15.0         6.0         27.0   \n",
       "1314                10.0         12.0         5.0         21.0   \n",
       "1317                10.0         13.0         4.0         20.0   \n",
       "1318                10.0         12.0         4.0         20.0   \n",
       "\n",
       "      PrecipitationSumInches  \n",
       "1305                    0.00  \n",
       "1306                    0.00  \n",
       "1307                    0.00  \n",
       "1308                    0.00  \n",
       "1311                    0.01  \n",
       "1312                    0.00  \n",
       "1313                    0.00  \n",
       "1314                    0.00  \n",
       "1317                    0.00  \n",
       "1318                    0.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last 10 rows of the dataframe:\n",
    "df_rainfall.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the unwanted column('Unnamed: 0):\n",
    "df_rainfall.drop(columns = [\"Unnamed: 0\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1008 entries, 1 to 1318\n",
      "Data columns (total 15 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   TempHighF                  1008 non-null   int64  \n",
      " 1   TempAvgF                   1008 non-null   int64  \n",
      " 2   TempLowF                   1008 non-null   int64  \n",
      " 3   DewPointAvgF               1008 non-null   float64\n",
      " 4   HumidityHighPercent        1008 non-null   float64\n",
      " 5   HumidityAvgPercent         1008 non-null   float64\n",
      " 6   HumidityLowPercent         1008 non-null   float64\n",
      " 7   SeaLevelPressureAvgInches  1008 non-null   float64\n",
      " 8   VisibilityHighMiles        1008 non-null   float64\n",
      " 9   VisibilityAvgMiles         1008 non-null   float64\n",
      " 10  VisibilityLowMiles         1008 non-null   float64\n",
      " 11  WindHighMPH                1008 non-null   float64\n",
      " 12  WindAvgMPH                 1008 non-null   float64\n",
      " 13  WindGustMPH                1008 non-null   float64\n",
      " 14  PrecipitationSumInches     1008 non-null   float64\n",
      "dtypes: float64(12), int64(3)\n",
      "memory usage: 126.0 KB\n"
     ]
    }
   ],
   "source": [
    "# An overview of the dataframe:\n",
    "df_rainfall.info()"
   ]
  },
  {
   "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>TempHighF</th>\n",
       "      <th>TempAvgF</th>\n",
       "      <th>TempLowF</th>\n",
       "      <th>DewPointAvgF</th>\n",
       "      <th>HumidityHighPercent</th>\n",
       "      <th>HumidityAvgPercent</th>\n",
       "      <th>HumidityLowPercent</th>\n",
       "      <th>SeaLevelPressureAvgInches</th>\n",
       "      <th>VisibilityHighMiles</th>\n",
       "      <th>VisibilityAvgMiles</th>\n",
       "      <th>VisibilityLowMiles</th>\n",
       "      <th>WindHighMPH</th>\n",
       "      <th>WindAvgMPH</th>\n",
       "      <th>WindGustMPH</th>\n",
       "      <th>PrecipitationSumInches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>30.13</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>27.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.49</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.45</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>40.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>30.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TempHighF  TempAvgF  TempLowF  DewPointAvgF  HumidityHighPercent  \\\n",
       "1         56        48        39          36.0                 93.0   \n",
       "2         58        45        32          27.0                 76.0   \n",
       "3         61        46        31          28.0                 89.0   \n",
       "4         58        50        41          40.0                 86.0   \n",
       "5         57        48        39          36.0                 79.0   \n",
       "\n",
       "   HumidityAvgPercent  HumidityLowPercent  SeaLevelPressureAvgInches  \\\n",
       "1                68.0                43.0                      30.13   \n",
       "2                52.0                27.0                      30.49   \n",
       "3                56.0                22.0                      30.45   \n",
       "4                71.0                56.0                      30.33   \n",
       "5                63.0                47.0                      30.40   \n",
       "\n",
       "   VisibilityHighMiles  VisibilityAvgMiles  VisibilityLowMiles  WindHighMPH  \\\n",
       "1                 10.0                10.0                 5.0         16.0   \n",
       "2                 10.0                10.0                10.0          8.0   \n",
       "3                 10.0                10.0                 7.0         12.0   \n",
       "4                 10.0                10.0                 7.0         10.0   \n",
       "5                 10.0                 9.0                 7.0         12.0   \n",
       "\n",
       "   WindAvgMPH  WindGustMPH  PrecipitationSumInches  \n",
       "1         6.0         25.0                     0.0  \n",
       "2         3.0         12.0                     0.0  \n",
       "3         4.0         20.0                     0.0  \n",
       "4         2.0         16.0                     0.0  \n",
       "5         3.0         17.0                     0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>TempHighF</th>\n",
       "      <th>TempAvgF</th>\n",
       "      <th>TempLowF</th>\n",
       "      <th>DewPointAvgF</th>\n",
       "      <th>HumidityHighPercent</th>\n",
       "      <th>HumidityAvgPercent</th>\n",
       "      <th>HumidityLowPercent</th>\n",
       "      <th>SeaLevelPressureAvgInches</th>\n",
       "      <th>VisibilityHighMiles</th>\n",
       "      <th>VisibilityAvgMiles</th>\n",
       "      <th>VisibilityLowMiles</th>\n",
       "      <th>WindHighMPH</th>\n",
       "      <th>WindAvgMPH</th>\n",
       "      <th>WindGustMPH</th>\n",
       "      <th>PrecipitationSumInches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>71.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>70.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>103</td>\n",
       "      <td>89</td>\n",
       "      <td>75</td>\n",
       "      <td>67.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>79</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>29.91</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>99</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>61.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TempHighF  TempAvgF  TempLowF  DewPointAvgF  HumidityHighPercent  \\\n",
       "1312        103        91        78          71.0                 91.0   \n",
       "1313        103        91        78          70.0                 91.0   \n",
       "1314        103        89        75          67.0                 82.0   \n",
       "1317        106        93        79          68.0                 69.0   \n",
       "1318         99        88        77          61.0                 64.0   \n",
       "\n",
       "      HumidityAvgPercent  HumidityLowPercent  SeaLevelPressureAvgInches  \\\n",
       "1312                61.0                31.0                      30.03   \n",
       "1313                58.0                25.0                      30.03   \n",
       "1314                54.0                25.0                      29.97   \n",
       "1317                48.0                27.0                      29.91   \n",
       "1318                43.0                22.0                      29.97   \n",
       "\n",
       "      VisibilityHighMiles  VisibilityAvgMiles  VisibilityLowMiles  \\\n",
       "1312                 10.0                10.0                 9.0   \n",
       "1313                 10.0                10.0                10.0   \n",
       "1314                 10.0                10.0                10.0   \n",
       "1317                 10.0                10.0                10.0   \n",
       "1318                 10.0                10.0                10.0   \n",
       "\n",
       "      WindHighMPH  WindAvgMPH  WindGustMPH  PrecipitationSumInches  \n",
       "1312         15.0         7.0         24.0                     0.0  \n",
       "1313         15.0         6.0         27.0                     0.0  \n",
       "1314         12.0         5.0         21.0                     0.0  \n",
       "1317         13.0         4.0         20.0                     0.0  \n",
       "1318         12.0         4.0         20.0                     0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TempHighF</th>\n",
       "      <th>TempAvgF</th>\n",
       "      <th>TempLowF</th>\n",
       "      <th>DewPointAvgF</th>\n",
       "      <th>HumidityHighPercent</th>\n",
       "      <th>HumidityAvgPercent</th>\n",
       "      <th>HumidityLowPercent</th>\n",
       "      <th>SeaLevelPressureAvgInches</th>\n",
       "      <th>VisibilityHighMiles</th>\n",
       "      <th>VisibilityAvgMiles</th>\n",
       "      <th>VisibilityLowMiles</th>\n",
       "      <th>WindHighMPH</th>\n",
       "      <th>WindAvgMPH</th>\n",
       "      <th>WindGustMPH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>30.13</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "      <td>45</td>\n",
       "      <td>32</td>\n",
       "      <td>27.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.49</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.45</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "      <td>40.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30.33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>30.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TempHighF  TempAvgF  TempLowF  DewPointAvgF  HumidityHighPercent  \\\n",
       "1         56        48        39          36.0                 93.0   \n",
       "2         58        45        32          27.0                 76.0   \n",
       "3         61        46        31          28.0                 89.0   \n",
       "4         58        50        41          40.0                 86.0   \n",
       "5         57        48        39          36.0                 79.0   \n",
       "\n",
       "   HumidityAvgPercent  HumidityLowPercent  SeaLevelPressureAvgInches  \\\n",
       "1                68.0                43.0                      30.13   \n",
       "2                52.0                27.0                      30.49   \n",
       "3                56.0                22.0                      30.45   \n",
       "4                71.0                56.0                      30.33   \n",
       "5                63.0                47.0                      30.40   \n",
       "\n",
       "   VisibilityHighMiles  VisibilityAvgMiles  VisibilityLowMiles  WindHighMPH  \\\n",
       "1                 10.0                10.0                 5.0         16.0   \n",
       "2                 10.0                10.0                10.0          8.0   \n",
       "3                 10.0                10.0                 7.0         12.0   \n",
       "4                 10.0                10.0                 7.0         10.0   \n",
       "5                 10.0                 9.0                 7.0         12.0   \n",
       "\n",
       "   WindAvgMPH  WindGustMPH  \n",
       "1         6.0         25.0  \n",
       "2         3.0         12.0  \n",
       "3         4.0         20.0  \n",
       "4         2.0         16.0  \n",
       "5         3.0         17.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the independent features:\n",
    "input_ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1008, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_ds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Distribution & Correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of all dependent variables.\n",
    "# Create a histogram of each:\n",
    "new_df = df_rainfall.drop(columns=[\"PrecipitationSumInches\"])\n",
    "distr_hist = new_df.hist(bins=15)\n",
    "\n",
    "# Disable axis labels to avoid clutter:\n",
    "for axis in distr_hist.flatten():\n",
    "    axis.set_xticklabels([])\n",
    "    axis.set_yticklabels([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'TempHighF' column:\n",
    "plt.boxplot(df_rainfall[\"TempHighF\"], vert = False)\n",
    "plt.xlabel(\"Highest Temperature [°F]\")\n",
    "plt.title(\"Distribution of Highest Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       79.078373\n",
       "std        15.522569\n",
       "min        32.000000\n",
       "25%        69.000000\n",
       "50%        81.000000\n",
       "75%        92.000000\n",
       "max       106.000000\n",
       "Name: TempHighF, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats of 'TempHighF' feature:\n",
    "df_rainfall[\"TempHighF\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'TempAvgF' column:\n",
    "plt.boxplot(df_rainfall[\"TempAvgF\"], vert = False)\n",
    "plt.xlabel(\"Average Temperature [°F]\")\n",
    "plt.title(\"Distribution of Average Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       68.929563\n",
       "std        14.742881\n",
       "min        29.000000\n",
       "25%        57.000000\n",
       "50%        71.000000\n",
       "75%        82.000000\n",
       "max        93.000000\n",
       "Name: TempAvgF, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats of 'TempAvgF' feature:\n",
    "df_rainfall[\"TempAvgF\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEWCAYAAABPON1ZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVzklEQVR4nO3de7RkZX3m8e/DRZGrkEYDcmmVRGVIBEUl4wXGEIMOMc5kvMUbiYmD4+AlGi9RJ2hQdDlRM8vlGI2KoiIj6qgYxxsKiETlLqAIQisoV2kUkEaB3/zxvoeuPpzuPsc+fc576O9nrVpn195V7/69Vbue2vXuqn1SVUiSxrXZYhcgSVo3g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAG9QJL8p4kr5+ntvZIclOSzfv1ryf5q/lou7f3hSTPm6/25rDeo5Jcl+SqhV63NCKDeh4lWZHkliQ3JrkhyTeTHJ7kzse5qg6vqn+YZVsHr+s2VfXjqtq2qm6fh9qPTPKRae0/sao+tKFtz7GOPYCXA3tX1W/PsPygJFcsZE2zkaSS7LWWZX/X31BvSrIqye0T1y9Y6FrnQ5LDknxjsevYVBjU8+9Pqmo7YE/gLcCrgPfP90qSbDHfbQ5iD+BnVXXNYhcyX6rqzf0NdVvgcOD0qetV9e8Wu77pFmLbuhtvvxtHVXmZpwuwAjh42rxHAncA+/TrxwBH9ellwInADcD1wKm0N89j+31uAW4CXgksBwp4PvBj4JSJeVv09r4OHA18G/gF8Blgp77sIOCKmeoFDgF+Bfy6r+/cifb+qk9vBrwO+BFwDfBhYIe+bKqO5/XargNeu47HaYd+/2t7e6/r7R/c+3xHr+OYGe57l35MLHtIr/kG4ALgyX3+/fu8zfr19wHXTNzvWOClE7W9H7gS+AlwFLB5X7YXcDLw897H4/v8U3r/b+51P30dfT8M+MbE9QcDX+7P/0XA0yaWHQO8G/hCb/c04LeBdwIrge8D+017Pl8DXNiXfxDYamL5ocA5/bH4JvD70+77KuA84FZgC+DVwA+BG3ub/2nicV4F3N7rumH69rKWvhbwIuBi4LL11eRlYrtZ7ALuThdmCOo+/8fAC/v0MawO6qOB9wBb9stjgczUFqvD8MPANsC9mDmofwLs02/zSeAjfdlBrCWo+/SRU7edWH7nCw/4S+AS4AHAtsCngGOn1fa+XtdD+4v9IWt5nD5MexPZrt/3B8Dz11bntPvOuLw/fpcAfwfcA3h8D5gHTTwHD+/TFwGXTtXXl+3Xpz8N/HN//O5De9P7r33ZccBraW8qWwGPmVh/AXvNYhs5jB5efR2XA39BC8b9aG8Ae09sK9cBD+/rOwm4DHgusDntTeRr057P84HdgZ1owT61re1He4N9VL/v8/rt7zlx33P6fe/V5z0V2LX39+m0N6Jdpvdjpu1lptv0x+jLvbZ7ra8mL6svDn0sjJ/SNs7pfg3sAuxZVb+uqlOrb9HrcGRV3VxVt6xl+bFVdX5V3Qy8Hnja1MHGDfQs4O1VdWlV3UTbc3vGtI+wb6iqW6rqXOBcWmCvodfyDOA1VXVjVa0A/hF4zgbWdwDtDeQtVfWrqjqJ9mnlmX35ycCBSabGvU/o1+8PbA+cm+S+wJNoe9c3Vxt+eUevF9rztSewa1WtqqoNHaM9FFhRVR+sqtuq6mzam+tTJ27z6ao6s6pW0d5EVlXVh6sdlzieFnaT3lVVl1fV9cCbJvr/AuCfq+pbVXV7tWMPt9Ietyn/q9/3FoCq+kRV/bSq7qiq42l7wo/cwD4fXVXX93XMpibhGPVCuR/to+10b6PtBX4pyaVJXj2Lti6fw/If0fY0l82qynXbtbc32fYWwH0n5k1+S+OXtOCcblmvaXpb95uH+i6vqjvW0u7JtL3xx9GGKr4OHNgvp/b77dlru7IfDL6Btnd9n97GK4EA305yQZK/3MCa9wQeNbWuvr5n0YY3plw9MX3LDNenP8bTn/9dJ9b18mnr2n1i+fT7kuS5Sc6ZuP0+bPi2NLmO2dQk2gtNG1GSR9DC4i57X1V1I+0bDi9Psg9wUpLvVNVXaR8TZ7K+Pe7dJ6b3oO0FXkf72Lr1RF2bAzvPod2f0l5Yk23fRguO3dZz30nXsXrP9MKJtn4yhzbWVt/uSTabCOs9aMMq0IL6bcAVffobtGGnVf06tBC5FVhWVbdNX0FVXQX8NUCSxwBfSXJKVV3yG9Z8OXByVf3Rb3j/mUx//n86sa43VdWb1nHfO7eBJHvShrL+kHbw8/Yk59DeqNa47YQ1tjHWfMO5yzpmWZNwj3qjSbJ9kkOBj9PGfr87w20OTbJXktAOUN1OO5AGLQAf8Bus+tlJ9k6yNfBG4IT+MfkHwFZJ/mOSLWkH8O45cb+rgeWTXyWc5jjgZUnun2Rb4M20g2l3CbR16bX8H+BNSbbrgfA3wEfWfc81Jdlq8kIbS/4l8MokWyY5CPgT2uNPVV1M2wN9Ni0cf9H7/Gf0oK6qK4EvAf/Yn7/NkjwwyYF9nU9NMvWmtJIWOhvyfJ0I/G6S5/Sat0zyiCQPmWM7k16UZLckO9HG04/v898HHJ7kUWm26dvCdmtpZxta/64FSPIXtD3qKVcDuyW5x8S8c4D/nGTr/lXF56+n1rnWtMkyqOff55LcSNtbeC3wdtrBopn8DvAV2pHz04F3V9XX+rKjgdf1j4SvmMP6j6UdhLqKdgDqxQBV9XPgvwH/Qtt7vZm2dznlE/3vz5KcNUO7H+htn0I7oLUKOGIOdU06oq//Utqe7cd6+7N1P1roTl52pwXzE2l77e8GnltV35+438m0r/5dPnE9wGR/n0s7GDn1zYkTaMcRAB4BfCvJTcBngZdU1aV92ZHAh/rz9bTZdKJ/onoCbQz8p7Tn7K2s+QY6Vx+jvdlcSvvGxlF9XWfQPg28q/frEtrBvrXVdiHt2MHptFD+PdrBySkn0b5Zc1WS6/q8d9C+PXQ18CHgo+sqdK41bcqmvmEgaYlLsoL2rYuvLHYtml/uUUvS4AxqSRqcQx+SNDj3qCVpcBvle9TLli2r5cuXb4ymJelu6cwzz7yuqnaeadlGCerly5dzxhlnbIymJeluKcmP1rbMoQ9JGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcAa1JA3OoJakwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcAa1JA3OoJakwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcAa1JA3OoJakwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcFssdgHSuuy0006sXLlyscsYSv399uQNv1jsMhbEjjvuyPXXX7/YZSw6g1pDW7lyJVW12GWM5cgdNpnHJMlilzAEhz4kaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4IYLar+OI2mp2lj5NVxQS5LWZFBL0uAMakkanEEtSYNbb1An+UCSa5KcvxAFSZLWNJs96mOAQzZyHZKktVhvUFfVKYDnGZSkRTJvpzlN8gLgBQB77LHHhrY1HyVJuhswD+YxqKvqvcB7Afbff/8NOlnupnKuXa2fL1ItpTzwBy+StIkyqCVpcLP5et5xwOnAg5JckeT5G78sSdKU9Y5RV9UzF6IQSdLMHPqQpMEZ1JI0OINakgY3XFAvpe9MStKkjZVfwwW1JGlNBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uDm7TSn0sbiqU7XVH+//SbzmOy4446LXcIQDGoNze/Vz6yOXOwKtJAc+pCkwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcAa1JA3OoJakwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcAa1JA3OoJakwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGZ1BL0uAMakkanEEtSYMzqCVpcAa1JA3OoJakwRnUkjQ4g1qSBmdQS9LgDGpJGpxBLUmDM6glaXAGtSQNzqCWpMEZ1JI0OINakgZnUEvS4AxqSRqcQS1JgzOoJWlwBrUkDc6glqTBGdSSNDiDWpIGl6qa/0aTa4EfzWOTy4Dr5rG9xWAfxmAfxmAf7mrPqtp5pgUbJajnW5Izqmr/xa5jQ9iHMdiHMdiHuXHoQ5IGZ1BL0uCWSlC/d7ELmAf2YQz2YQz2YQ6WxBi1JG3KlsoetSRtsgxqSRrcUEGdZPckX0tyYZILkrykz98pyZeTXNz/7rjYta5Nkq2SfDvJub0Pb+jz75/kW0kuSXJ8knssdq3rk2TzJGcnObFfX1J9SLIiyXeTnJPkjD5vyWxLU5LcO8kJSb6f5HtJ/mAp9SPJg/pzMHX5RZKXLqU+ACR5WX9Nn5/kuP5aX5DXxFBBDdwGvLyq9gYOAF6UZG/g1cBXq+p3gK/266O6FXh8VT0U2Bc4JMkBwFuBd1TVXsBK4PmLV+KsvQT43sT1pdiH/1BV+05833UpbUtT/gn4f1X1YOChtOdkyfSjqi7qz8G+wMOBXwKfZgn1Icn9gBcD+1fVPsDmwDNYqNdEVQ17AT4D/BFwEbBLn7cLcNFi1zbL+rcGzgIeRfsF0xZ9/h8AX1zs+tZT+260F8/jgROBLME+rACWTZu3pLYlYAfgMvqB/6Xaj4m6nwCcttT6ANwPuBzYCdiivyb+eKFeE6PtUd8pyXJgP+BbwH2r6sq+6CrgvotV12z0IYNzgGuALwM/BG6oqtv6Ta6gPfEjeyfwSuCOfv23WHp9KOBLSc5M8oI+b0ltS8D9gWuBD/ZhqH9Jsg1Lrx9TngEc16eXTB+q6ifA/wR+DFwJ/Bw4kwV6TQwZ1Em2BT4JvLSqfjG5rNpb19DfKayq26t9zNsNeCTw4MWtaG6SHApcU1VnLnYtG+gxVfUw4Im0YbTHTS5cCtsSbe/tYcD/rqr9gJuZNkSwRPpBH799MvCJ6ctG70MfP/9T2hvnrsA2wCELtf7hgjrJlrSQ/mhVfarPvjrJLn35LrQ91eFV1Q3A12gfie6dZIu+aDfgJ4tV1yw8GnhykhXAx2nDH//E0urD1F4QVXUNbUz0kSy9bekK4Iqq+la/fgItuJdaP6C9YZ5VVVf360upDwcDl1XVtVX1a+BTtNfJgrwmhgrqJAHeD3yvqt4+seizwPP69PNoY9dDSrJzknv36XvRxti/Rwvs/9JvNnQfquo1VbVbVS2nfVQ9qaqexRLqQ5Jtkmw3NU0bGz2fJbQtAVTVVcDlSR7UZ/0hcCFLrB/dM1k97AFLqw8/Bg5IsnXPqannYWFeE4s9SD9twP4xtI8/5wHn9MuTaOOjXwUuBr4C7LTYta6jD78PnN37cD7wP/r8BwDfBi6hffS752LXOsv+HAScuNT60Gs9t18uAF7b5y+ZbWmiL/sCZ/Rt6v8COy61ftCGCn4G7DAxb6n14Q3A9/vr+ljgngv1mvAn5JI0uKGGPiRJd2VQS9LgDGpJGpxBLUmDM6glaXAG9SYuyU2LtN7lSf58hvm/N3GWteuTXNanv7IYda5Pkqf0E4dtjLYPSvLzJP86Me9lSc5K8vSJebdPOzvd8iSPTTsL5fkbozYtLINai2U5cJegrqrv1uozrX0W+Nt+/eAFru9OSTZfx+KnAHMK6olfss3GqVX1pH6/bYFH0H5hOfnY3TL1mPXLiqo6lfYbBN0NGNS6iyT7Jvm3JOcl+XSSHZPcJ8mZfflDk1SSPfr1H/ZfbO2c5JNJvtMvj+7LD5zY2zu7/2LwLcBj+7yXzaKmJyQ5ve9NfqKH1tQ5p4/u7ZyR5GFJvthrOrzf5qAkpyT5fJKLkrwnyWazaPetSc4Cnprkr3ufzu193DrJv6edu+Jtff0PTPL1JPv3Npb1n+GT5LAkn01yEvDV/svJD6Sdu/zsJH86m6em//XHD5uaxf61j5fFvQA3zTDvPODAPv1G4J19+gJge+C/A98BngXsCZzel3+MdiIkgD1opwIA+Bzw6D69Le1EQwfRf/G4jtqOof08dxlwCrBNn/8qVv/icwXwwj79jl77dsDOwNV9/kHAKtqvyDanndFwNu2+cqKW35qYPgo4YrLGiWVfp52zmN7+ij59GO28HTv1628Gnt2n7w38YKqOibbu8hgBr6H98vXPJ+bdzupf8n56Yv5y4PzF3sa8bPhlLh/BtAlIsgNw76o6uc/6EKvPdvZN2oloHkcLmkNoe3mn9uUHA3u3UyEAsH3fQz0NeHuSjwKfqqorJm4zGwfQhhdO6/e7B3D6xPLP9r/fBbatqhuBG5PcOnXeFeDbVXVp7+NxtNMVrFpPu8dPTO+T5ChaqG4LfHEuHei+XFXX9+kn0E589Yp+fSv6m9u6Gqiqo4Gjp82+pdpQke6mDGrNxSnAY2l70Z+h7YEW8Pm+fDPggKpaNe1+b0nyedqY6WlJ/niO6w0t5J65luW39r93TExPXZ/axqcPF9Qs2r15YvoY4ClVdW6Sw2h7uzO5jdVDiluto70Af1ZVF62lHelOjlFrDVX1c2Blksf2Wc8BpvauTwWeDVxcVXcA19PC9xt9+ZeAI6baSrJv//vAagcJ30obMnkwcCNtiGI2/g14dJK9envbJPndOXbtkWn/324z4Om95rm0ux1wZdppeJ81MX96P1bQ/t0UrD6r2ky+CByRviufZL859kebEINaWye5YuLyN7TTNb4tyXm0M7e9EaCqVtD2BE/p9/0G7T9crOzXXwzs3w9CXggc3ue/NO0fgp4H/Br4Am0s+fZ+cG6dBxOr6lraGO9xvY3Tmfs/Y/gO8C7a0MJltLHcubT7etp/GzqNdga1KR8H/rYfEHwg7b+AvDDJ2bQx6rX5B2BL4LwkF/Tr0ow8e57u9pIcBLyiqg5d5FLmZEPrTvt3didW+2esWsLco5bG9SvaQcx/Xe8tp+lDV5+j/fNVLXHuUUvS4NyjlqTBGdSSNDiDWpIGZ1BL0uAMakka3P8HTcqluXs4/SIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'TempLowF' column:\n",
    "plt.boxplot(df_rainfall[\"TempLowF\"], vert = False)\n",
    "plt.xlabel(\"Lowest Temperature [°F]\")\n",
    "plt.title(\"Distribution of Lowest Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       58.269841\n",
       "std        14.835229\n",
       "min        19.000000\n",
       "25%        46.000000\n",
       "50%        60.000000\n",
       "75%        72.000000\n",
       "max        81.000000\n",
       "Name: TempLowF, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats of 'TempLowF' feature:\n",
    "df_rainfall[\"TempLowF\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'DewPointAvgF' column:\n",
    "plt.boxplot(df_rainfall[\"DewPointAvgF\"], vert = False)\n",
    "plt.xlabel(\"Average Dew Point Temperature [°F]\")\n",
    "plt.title(\"Distribution of Average Dew Point Temperature\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       54.822421\n",
       "std        15.690123\n",
       "min         8.000000\n",
       "25%        43.000000\n",
       "50%        59.000000\n",
       "75%        69.000000\n",
       "max        76.000000\n",
       "Name: DewPointAvgF, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"DewPointAvgF\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzArmEDczK5hD3MysYA5xM7OCOcTNzAqmiBj6hUorgIeGfMHNjQWe2YrrG4ySaoWy6i2pViir3pJqhbLqbdS6b0SMG+jMwxLiW5ukJRExtdV19EdJtUJZ9ZZUK5RVb0m1Qln1DrZWd6eYmRXMIW5mVrBtJcS/0+oCBqCkWqGsekuqFcqqt6Raoax6B1XrNtEnbma2vdpWjsTNzLZLDnEzs4IVFeKSRkm6U9IySfdK+nyePkHSLyT9TtLVkl7T6lqrJI2Q9EtJN+TbtaxX0oOS7pF0t6QleVq7pJsk/Tb/3aPVdTZI2l3StZLuk/RrSYfVsV5Jb83btHH5g6RP1LHWBkmfzP9jyyUtyP97dd1vP57rvFfSJ/K02mxbSZdLelrS8sq0HutT8o28jX8l6eC+ll9UiAMvA++JiLcBk4HjJR0K/A3wtYjYD3gOOLN1Jfbo48CvK7frXO9RETG58rnVvwJ+FhFvBn6Wb9fFJcCNEbE/8DbSNq5dvRHxm7xNJwNTgDXA/6aGtQJI2hv4S2BqREwCRgCnUMP9VtIkYAZwCGkfOFHSftRr214BHN9tWrP6TgDenC9nAZf2ufSIKPICjAbuAt5B+rbTyDz9MOBfW11fpc598pP0HuAGQHWtF3gQGNtt2m+AN+TrbwB+0+o6cy1jgAfIb87Xvd5KfccCt9W5VmBv4BGgHRiZ99vj6rjfAh8Evle5fT7w2bptW6ADWF653WN9wLeB6T21a3Yp7Ui80TVxN/A0cBPw/4BVEfFKbvIoaSesi6+TdqoN+fae1LfeAH4qaamks/K010XEE/n6k8DrWlPaFiYAK4Dv566qyyTtTH3rbTgFWJCv17LWiHgM+CrwMPAE8DywlHrut8uBIyTtKWk08F7gjdR021Y0q6/xAtrQ53YuLsQjYn2k09J9SKdQ+7e2ouYknQg8HRFLW11LPx0eEQeTTun+QtK7qndGOjSoy2dSRwIHA5dGxB8DXXQ7Za5ZveQ+5PcB13S/r0615v7Z95NeKPcCdmbL7oBaiIhfk7p5fgrcCNwNrO/WpjbbtieDra+4EG+IiFXAYtJp3e6SRua79gEea1Vd3bwTeJ+kB4EfkrpULqGm9eYjMCLiaVKf7SHAU5LeAJD/Pt26CjfzKPBoRPwi376WFOp1rRfSi+NdEfFUvl3XWo8BHoiIFRGxDvhn0r5c1/32exExJSLeReqrv5/6btuGZvU9RjqTaOhzOxcV4pLGSdo9X98J+K+kN7MWAx/IzU4HftSSAruJiHMjYp+I6CCdRi+KiFOpYb2Sdpa0a+M6qe92ObCQVCPUpFaAiHgSeETSW/Oko4H/pKb1ZtPZ1JUC9a31YeBQSaMliU3btnb7LYCk1+a/44H/Dsynvtu2oVl9C4GP5E+pHAo8X+l26Vmr35gY4JsD/wX4JfArUsBckKe/CbgT+B3pVLWt1bX2UPuRwA11rTfXtCxf7gXm5Ol7kt6Y/S1wM9De6lorNU8GluT94Xpgj7rWS+qSWAmMqUyrZa25ts8D9+X/syuBtjrut7nWW0gvMsuAo+u2bUkv3E8A60hnkGc2q4/0wYe/J73Xdw/pE0K9Lt9fuzczK1hR3SlmZrY5h7iZWcEc4mZmBXOIm5kVzCFuZlYwh7g1JWmapJBU22/FNuQRGMdWbh/ZGDVyCJb9Pkk9DqAk6YX8dy9J1+brkyW9d4DrmCvpMUlfyLf/NI/Kd4ukPfO0P5J0dWWenfKoiGurj922Lw5x68104Nb8d9AkjRiK5WxtEbEwIr7SR5vHI6LxRZjJpDE8BuprEXFBvj4LeDtpQKQP5WkXAedV1vlipCEoHn8V67JthEPceiRpF+Bw0hcTTsnTjpd0TaXNxqNdScdKul3SXZKuyfM3jpD/RtJdwAclzZD0H0pjwl+XBy1qHGXeoTSe+UWNI9x832fyPL9SHkN+gI9lrqRPV24vl9SRL/dJukLS/ZL+UdIxkm7L4zwfktufIemb+fqE/DjvkXRRZZkdebmvAb4AnJyPkk/OyxqX2+2gNFb0uD7K3kD6gs1oYJ2kI4AnI+K3A338tm1ziFsz7yeN1X0/sFLSFNI3y96Rv5YPcDLww3wqfx5wTKQBtJYA51SWtTIiDo6IHwL/HBFvjzQm/K/ZNCb1JcAlEXEQ6VttQHpxII2tfAjpCHdK94G5Khbn4LwbuKyfj3M/oJM0kNr+pKPew4FPA5/rof0lpEG3DiJ9C28zEbEWuAC4OtIY4lcDVwGn5ibHAMsiYkUfdX2ZtL1PIn3j73zgi/18TLYdcYhbM9NJg3aR/06PNAzpjcBJeSCkPyGN+XAocCBwWw7Q04F9K8u6unJ9Uu7nvYcUbBPz9MPYNLrf/Er7Y/Pll6Tx4/cnhXpPGj9oMRn4834+zgci4p6I2EAabuBnkb7GfA9pDOju3smm8U+u7Oc6Lgc+kq9/FPh+XzNExE2RBnU6ifSC+hPgLUq/ZPTdxhmM2ci+m9j2RlI7acTFgyQF6ZddQtJnSIE+E3gWWBIRq/MgSTdFRLO+867K9SuAaRGxTNIZpDFlei0H+HJEfPvVPh7gFTY/YBlVuf5y5fqGyu0NNP//GNBYFRHxiKSnJL2HdEZxal/zNOSwPoP0oww3kAZ4+kBexncHUodtm3wkbj35AHBlROwbER0R8UbSr+gcAfw7acjXGWw6Ur8DeKfSz2I1RkR8S5Nl7wo8IWlHNg+zO4A/zddPqUz/V+CjlT72vRuj1g3Ag7lmlH6zcMIA56+6rVJfszBeTXqcVZeRulWuiYj1W87S1GeAb0QaEnYn0gvIBlJfuZlD3Ho0nTSeeNV1pC6V9aQjwhPyX3L/7hnAAkm/Am6n+Y91nA/8ghSG91WmfwI4J8+/H+nXZIiIn5K6V27PXTDXsmVA9uU6oF3SvaSziPsHOH/Vx0k/mHEPzX9xZTFwYOONzTxtIbAL/ehKaZC0F3BIRFyfJ/0d8B/Ax9i8y8m2Yx7F0Gohdxu8GBEh6RTSC8b7W13XUJE0lfQRwiOa3D8XeCEivvoqlv0gacjSZwZVpBXJfeJWF1OAb+b+9VWkNwC3CfmLQmfTe1/4C8BZknarfFa8r+XuRDrr2ZFNv+Fq2xkfiZuZFcx94mZmBXOIm5kVzCFuZlYwh7iZWcEc4mZmBfv/uJM1Xs9vIS0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'HumidityAvgPercent' column:\n",
    "plt.boxplot(df_rainfall[\"HumidityAvgPercent\"], vert = False)\n",
    "plt.xlabel(\"Average Humidity [%]\")\n",
    "plt.title(\"Distribution of Average Humidity\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       66.210317\n",
       "std        12.755567\n",
       "min        27.000000\n",
       "25%        58.000000\n",
       "50%        67.000000\n",
       "75%        74.000000\n",
       "max        97.000000\n",
       "Name: HumidityAvgPercent, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"HumidityAvgPercent\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'SeaLevelPressureAvgInches' column:\n",
    "plt.boxplot(df_rainfall[\"SeaLevelPressureAvgInches\"], vert = False)\n",
    "plt.xlabel(\"Average Sea Level Pressure [Inches]\")\n",
    "plt.title(\"Distribution of Average Sea Level Pressure\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       30.081349\n",
       "std         0.146358\n",
       "min        29.910000\n",
       "25%        29.970000\n",
       "50%        30.040000\n",
       "75%        30.150000\n",
       "max        30.740000\n",
       "Name: SeaLevelPressureAvgInches, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"SeaLevelPressureAvgInches\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'VisibilityHighMiles' column:\n",
    "plt.boxplot(df_rainfall[\"VisibilityHighMiles\"], vert = False)\n",
    "plt.xlabel(\"Highest Visibility [Miles]\")\n",
    "plt.title(\"Distribution of Highest Visibility\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean        9.994048\n",
       "std         0.099474\n",
       "min         8.000000\n",
       "25%        10.000000\n",
       "50%        10.000000\n",
       "75%        10.000000\n",
       "max        10.000000\n",
       "Name: VisibilityHighMiles, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"VisibilityHighMiles\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'VisibilityAvgMiles' column:\n",
    "plt.boxplot(df_rainfall[\"VisibilityAvgMiles\"], vert = False)\n",
    "plt.xlabel(\"Average Visibility [Miles]\")\n",
    "plt.title(\"Distribution of Average Visibility\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean        9.230159\n",
       "std         1.435333\n",
       "min         2.000000\n",
       "25%         9.000000\n",
       "50%        10.000000\n",
       "75%        10.000000\n",
       "max        10.000000\n",
       "Name: VisibilityAvgMiles, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"VisibilityAvgMiles\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'WindAvgMPH' column:\n",
    "plt.boxplot(df_rainfall[\"WindAvgMPH\"], vert = False)\n",
    "plt.xlabel(\"Average Wind Speed [MPH]\")\n",
    "plt.title(\"Distribution of Average Wind Speed\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean        4.767857\n",
       "std         2.029494\n",
       "min         1.000000\n",
       "25%         3.000000\n",
       "50%         5.000000\n",
       "75%         6.000000\n",
       "max        12.000000\n",
       "Name: WindAvgMPH, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"WindAvgMPH\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'WindGustMPH' column:\n",
    "plt.boxplot(df_rainfall[\"WindGustMPH\"], vert = False)\n",
    "plt.xlabel(\"Wind Gust [MPH]\")\n",
    "plt.title(\"Distribution of Wind Gust\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean       20.609127\n",
       "std         5.677006\n",
       "min         0.000000\n",
       "25%        17.000000\n",
       "50%        20.000000\n",
       "75%        24.000000\n",
       "max        57.000000\n",
       "Name: WindGustMPH, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rainfall[\"WindGustMPH\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal boxplot to check for outliers in the 'PrecipitationSumInches' column:\n",
    "plt.boxplot(df_rainfall[\"PrecipitationSumInches\"], vert = False)\n",
    "plt.xlabel(\"Amount of Precipitation (Inches)\")\n",
    "plt.title(\"Distribution of Precipitation\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1008.000000\n",
       "mean        0.087073\n",
       "std         0.306352\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max         3.840000\n",
       "Name: PrecipitationSumInches, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats of 'PrecipitationSumInches' attribute:\n",
    "df_rainfall[\"PrecipitationSumInches\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix.\n",
    "# Shows the relationship between the dependent variables and the independent variable:\n",
    "corrmatrix = df_rainfall.corr()\n",
    "fig = plt.figure(figsize = (12, 9))\n",
    "\n",
    "sns.heatmap(corrmatrix, vmax = 1.0, square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot to test if 'HumidityAvgPercent' influences the amount of precipitation:\n",
    "plt.scatter(df_rainfall[\"HumidityAvgPercent\"], df_rainfall[\"PrecipitationSumInches\"])\n",
    "plt.xlabel(\"Average Humidity (%)\")\n",
    "plt.ylabel(\"Amount of Precipitation (Inches)\")\n",
    "plt.title(\"Precipitation vs. Average Humidy\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson correlation coefficient is:  0.3681990786463334\n"
     ]
    }
   ],
   "source": [
    "# The Pearson correlation coefficient of 'PrecipitationSumInches' and 'HumidityAvgPercent':\n",
    "p_correlation = df_rainfall[\"HumidityAvgPercent\"].corr(df_rainfall[\"PrecipitationSumInches\"])\n",
    "print(\"The Pearson correlation coefficient is: \", p_correlation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training & Test Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1008, 14) (1008,)\n"
     ]
    }
   ],
   "source": [
    "# Dataset summary:\n",
    "print(input_ds.shape, output_ds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input train:  (806, 14)\n",
      "\n",
      "input test:  (202, 14)\n",
      "\n",
      "output train:  (806,)\n",
      "\n",
      "output test:  (202,)\n"
     ]
    }
   ],
   "source": [
    "# Split into train and test sets:\n",
    "input_train, input_test, output_train, output_test = train_test_split(input_ds, output_ds, test_size = 0.2, random_state = 42)\n",
    "\n",
    "print(\"input train: \", input_train.shape)\n",
    "print(\"\\ninput test: \", input_test.shape)\n",
    "print(\"\\noutput train: \", output_train.shape)\n",
    "print(\"\\noutput test: \", output_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the training and test set:\n",
    "sc = StandardScaler()\n",
    "input_train = sc.fit_transform(input_train)\n",
    "input_test = sc.transform(input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.05255844 -1.23480713 -1.38764384 -2.05959806 -2.63694306 -2.23259463\n",
      "  -1.56512579  1.58762965  0.05761512  0.54358645  0.83061291  0.67215627\n",
      "   0.62588596  0.78520543]\n",
      " [-1.69273509 -1.23480713 -0.7194392  -0.72643975  0.77073194  0.68428437\n",
      "   0.54127174  0.425014    0.05761512 -0.90477943 -1.39447727  0.67215627\n",
      "  -0.36520261  0.25233236]\n",
      " [ 1.12404218  0.98464145  0.75061103  0.54323484 -0.79495657 -0.85517955\n",
      "  -0.72256678 -0.6692125   0.05761512  0.54358645  0.83061291 -0.86169977\n",
      "  -0.36520261 -0.63578941]\n",
      " [ 0.3558302  -0.15871085 -0.7194392  -0.66295602 -0.1502613  -0.93620396\n",
      "  -1.32439464 -0.46404503  0.05761512  0.54358645  0.83061291 -1.16847097\n",
      "  -0.8607469  -0.2805407 ]\n",
      " [ 0.1637772   0.37933729  0.55014963  0.54323484  1.13912924  0.84633321\n",
      "   0.54127174  0.425014    0.05761512 -0.18059649 -0.56006845  0.36538506\n",
      "   0.62588596  0.42995672]\n",
      " [-2.07684108 -1.97462333 -1.78856663 -1.61521196  0.40233464  0.36018671\n",
      "   0.2403578   1.17729471  0.05761512  0.54358645 -0.0037959   0.36538506\n",
      "  -0.36520261  0.07470801]\n",
      " [ 1.18805985  1.1864095   1.08471335  0.98762094  0.58653329 -0.20698421\n",
      "  -0.72256678 -0.53243419  0.05761512  0.54358645  0.55247664  0.05861385\n",
      "   0.62588596  0.25233236]\n",
      " [ 0.80395386  0.98464145  1.08471335  1.05110467 -0.05816198  0.52223554\n",
      "   0.84218567 -0.73760165  0.05761512  0.54358645  0.27434037 -1.16847097\n",
      "  -0.8607469  -0.63578941]\n",
      " [ 0.86797152  0.98464145  1.01789288  1.1145884   1.13912924  0.60325996\n",
      "   0.11999223 -1.14793659  0.05761512 -0.90477943 -1.67261354 -0.86169977\n",
      "  -0.8607469  -0.63578941]\n",
      " [ 1.12404218  1.11915349  1.01789288  0.98762094  0.58653329 -0.04493538\n",
      "  -0.42165285 -1.01115828  0.05761512  0.54358645  0.27434037 -0.24815735\n",
      "   0.13034167 -0.2805407 ]]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 10 rows of the 'input_train' 2D array:\n",
    "print(input_train[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean precipitation level is:  0.09\n",
      "Baseline MAE is:  0.14\n",
      "Baseline RMSE is:  0.3\n"
     ]
    }
   ],
   "source": [
    "y_train_mean = output_train.mean()\n",
    "y_pred_baseline = [y_train_mean] * len(output_train)\n",
    "mae_baseline = mean_absolute_error(output_train, y_pred_baseline)\n",
    "rmse_baseline = np.sqrt(mean_squared_error(output_train, y_pred_baseline))\n",
    "print(\"Mean precipitation level is: \", round(y_train_mean, 2))\n",
    "print(\"Baseline MAE is: \", round(mae_baseline, 2))\n",
    "print(\"Baseline RMSE is: \", round(rmse_baseline, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select & Train a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Multivariate Linear Regression (MLR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept is:  0.08528535980148888\n",
      "\n",
      "The coefficient is:  [ 0.24571219 -0.33747861  0.31965307 -0.22037346  0.12773646 -0.25306266\n",
      "  0.27313453 -0.00437155  0.02615958 -0.10718213 -0.02063717  0.07573155\n",
      " -0.07114171  0.02975905]\n"
     ]
    }
   ],
   "source": [
    "# Create a linear regression model and train it:\n",
    "model = LinearRegression()\n",
    "model.fit(input_train, output_train)\n",
    "\n",
    "# Calculating the intercept and coefficient:\n",
    "print(\"The intercept is: \", model.intercept_)\n",
    "print(\"\\nThe coefficient is: \", model.coef_)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_lr = model.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.12 \n",
      "\n",
      "Scores RMSE:  [0.2380799  0.20186494 0.18686664 0.37246955 0.2041969  0.17203585\n",
      " 0.45722685 0.16922172 0.19475157 0.17712903]\n",
      "Standard deviation:  0.09266019734473513 \n",
      "\n",
      "Mean RMSE is:  0.24\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_lr)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(model, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Multi-layer Perceptron Regressor (Neural Network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the multilayer perceptron regressor model.\n",
    "# The neural network is trained using standardized data:\n",
    "mlpr = MLPRegressor(hidden_layer_sizes = (16,), solver = 'lbfgs', max_iter = 10000)\n",
    "mlpr.fit(input_train, output_train)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_mlpr = mlpr.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.06 \n",
      "\n",
      "Scores RMSE:  [0.74032116 0.33451035 0.15891664 0.40775044 0.36928401 0.22366196\n",
      " 0.5455163  0.29952198 0.78789584 0.21393951]\n",
      "Standard deviation:  0.2064021486564087 \n",
      "\n",
      "Mean RMSE is:  0.41\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_mlpr)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(mlpr, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. KNeighbors Regressor (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a KNeighbors Regression model and train it:\n",
    "kn_reg = KNeighborsRegressor(n_neighbors = 2)\n",
    "kn_reg.fit(input_train, output_train)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_knn = kn_reg.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.04 \n",
      "\n",
      "Scores RMSE:  [0.23882557 0.24624025 0.15695993 0.33372616 0.34425443 0.23153074\n",
      " 0.36634768 0.18402615 0.1782642  0.17060737]\n",
      "Standard deviation:  0.07357095109061319 \n",
      "\n",
      "Mean RMSE is:  0.25\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_knn)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(kn_reg, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Ridge Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Different Parameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter 1 (alpha = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept is:  0.08528535980148894\n",
      "\n",
      "The coefficient is:  [ 0.04992996  0.01210887  0.12862574 -0.19040192  0.06355513 -0.11830609\n",
      "  0.17414104 -0.00417321  0.02595241 -0.1060957  -0.02147047  0.07447056\n",
      " -0.07048576  0.03020105]\n"
     ]
    }
   ],
   "source": [
    "# Create a ridge regression model and train it:\n",
    "ridge = Ridge()\n",
    "ridge.fit(input_train, output_train)\n",
    "\n",
    "# Calculating the intercept and coefficient:\n",
    "print(\"The intercept is: \", ridge.intercept_)\n",
    "print(\"\\nThe coefficient is: \", ridge.coef_)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_r = ridge.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.12 \n",
      "\n",
      "Scores RMSE:  [0.23829956 0.20051459 0.18617347 0.37378345 0.20453977 0.17088139\n",
      " 0.45457161 0.16833494 0.19585456 0.1775039 ]\n",
      "Standard deviation:  0.0923776310692345 \n",
      "\n",
      "Mean RMSE is:  0.24\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_r)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(ridge, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter 2 (alpha = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept is:  0.08528535980148885\n",
      "\n",
      "The coefficient is:  [-0.00220235  0.01693353  0.04189331 -0.04477622 -0.01289655  0.00819891\n",
      "  0.05128808 -0.00353129  0.0238549  -0.09865524 -0.02775774  0.06081555\n",
      " -0.06079119  0.03464541]\n"
     ]
    }
   ],
   "source": [
    "# Create a ridge regression model and train it:\n",
    "ridge25 = Ridge(alpha = 25)\n",
    "ridge25.fit(input_train, output_train)\n",
    "\n",
    "# Calculating the intercept and coefficient:\n",
    "print(\"The intercept is: \", ridge25.intercept_)\n",
    "print(\"\\nThe coefficient is: \", ridge25.coef_)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_r25 = ridge25.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.12 \n",
      "\n",
      "Scores RMSE:  [0.23662402 0.19501727 0.18709533 0.37870844 0.20212096 0.17091138\n",
      " 0.45140912 0.1678608  0.19772078 0.17910838]\n",
      "Standard deviation:  0.0925004471929151 \n",
      "\n",
      "Mean RMSE is:  0.24\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_r25)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(ridge25, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a Random Forest Regressor model and train it:\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(input_train, output_train)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_forest = forest_reg.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.03 \n",
      "\n",
      "Scores RMSE:  [0.23818096 0.190982   0.21263093 0.32130152 0.25443338 0.1786811\n",
      " 0.42100321 0.1163081  0.27353435 0.13801621]\n",
      "Standard deviation:  0.08545103243206881 \n",
      "\n",
      "Mean RMSE is:  0.23\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_forest)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(forest_reg, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Different Parameters:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter 1 (kernel = \"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a Support Vector Regressor model and train it:\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "regressor_linear = SVR(kernel = \"linear\")\n",
    "regressor_linear.fit(input_train, output_train)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_svr_l = regressor_linear.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.11 \n",
      "\n",
      "Scores RMSE:  [0.25175962 0.17643558 0.1997258  0.41529319 0.18913161 0.17780183\n",
      " 0.48623003 0.17697073 0.20984416 0.16589841]\n",
      "Standard deviation:  0.10665176354804083 \n",
      "\n",
      "Mean RMSE is:  0.24\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_svr_l)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(regressor_linear, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter 2 (kernel = \"rbf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a Support Vector Regressor model and train it:\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "regressor_rbf = SVR(kernel = \"rbf\")\n",
    "regressor_rbf.fit(input_train, output_train)\n",
    "\n",
    "# Make predictions:\n",
    "predictions_svr_r = regressor_rbf.predict(input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.09 \n",
      "\n",
      "Scores RMSE:  [0.21991073 0.17131496 0.1580901  0.32496973 0.1814094  0.16502927\n",
      " 0.41999116 0.13791165 0.18816222 0.13328967]\n",
      "Standard deviation:  0.08717889593176742 \n",
      "\n",
      "Mean RMSE is:  0.21\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the training set.\n",
    "# The Mean Absolute Error:\n",
    "mae_training = mean_absolute_error(output_train, predictions_svr_r)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "scores = cross_val_score(regressor_rbf, input_train, output_train, scoring = \"neg_mean_squared_error\", cv=10)\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "# Let's look at the results:\n",
    "def display_scores(scores):\n",
    "    print(\"Scores RMSE: \", scores)\n",
    "    print(\"Standard deviation: \", scores.std(), \"\\n\")\n",
    "    print(\"Mean RMSE is: \", round(scores.mean(), 2))\n",
    "    \n",
    "\n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tune The Models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, 4], &#x27;n_estimators&#x27;: [3, 6, 9]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, 4], &#x27;n_estimators&#x27;: [3, 6, 9]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [1, 2, 3, 4], 'n_estimators': [3, 6, 9]})"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using 'GridSearchCV' to find the best hyperparameters for the model:\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {\n",
    "    \"n_estimators\" : [3, 6, 9],\n",
    "    \"max_depth\" : [1, 2, 3, 4],\n",
    "}\n",
    " \n",
    "clf = GridSearchCV(forest_reg, parameters)\n",
    "clf.fit(input_train, output_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.08 \n",
      "\n",
      "The training RMSE is:  0 2\n"
     ]
    }
   ],
   "source": [
    "# Make predictions using best hyperparameters:\n",
    "y_pred_train = clf.predict(input_train)\n",
    "\n",
    "# Evaluate model performance using MAE and RMSE:\n",
    "mae_training = mean_absolute_error(output_train, y_pred_train)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "rmse_training = np.sqrt(mean_squared_error(output_train, y_pred_train))\n",
    "print(\"The training RMSE is: \", round(rmse_training), 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine ('rbf' kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVR(),\n",
       "             param_grid={&#x27;C&#x27;: [0.25, 0.5, 1], &#x27;epsilon&#x27;: [0.25, 0.5, 1],\n",
       "                         &#x27;gamma&#x27;: [1e-07, 0.0001]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVR(),\n",
       "             param_grid={&#x27;C&#x27;: [0.25, 0.5, 1], &#x27;epsilon&#x27;: [0.25, 0.5, 1],\n",
       "                         &#x27;gamma&#x27;: [1e-07, 0.0001]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVR(),\n",
       "             param_grid={'C': [0.25, 0.5, 1], 'epsilon': [0.25, 0.5, 1],\n",
       "                         'gamma': [1e-07, 0.0001]})"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using 'GridSearchCV' to find the best hyperparameters for the model:\n",
    "parameters = {\n",
    "    \"gamma\": [1e-7, 1e-4],\n",
    "    \"C\": [0.25, 0.5, 1],\n",
    "    \"epsilon\": [0.25, 0.5, 1],\n",
    "}\n",
    "\n",
    "clf = GridSearchCV(regressor_rbf, parameters)\n",
    "clf.fit(input_train, output_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training MAE is:  0.25 \n",
      "\n",
      "The training RMSE is:  0 2\n"
     ]
    }
   ],
   "source": [
    "# Make predictions using best hyperparameters:\n",
    "y_pred_train = clf.predict(input_train)\n",
    "\n",
    "# Evaluate model performance using MAE and RMSE:\n",
    "mae_training = mean_absolute_error(output_train, y_pred_train)\n",
    "print(\"The training MAE is: \", round(mae_training, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "rmse_training = np.sqrt(mean_squared_error(output_train, y_pred_train))\n",
    "print(\"The training RMSE is: \", round(rmse_training), 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Present The Solution (Evaluate On The Test Set)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Random Forest Regressor (Not Tweaked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test MAE is:  0.09 \n",
      "\n",
      "The test RMSE is:  0 2\n"
     ]
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred = forest_reg.predict(input_test)\n",
    "\n",
    "# Evaluate model performance using MAE and RMSE:\n",
    "mae_test = mean_absolute_error(output_test, y_pred)\n",
    "print(\"The test MAE is: \", round(mae_test, 2), \"\\n\")\n",
    "# The Root Mean Squared Error:\n",
    "rmse_test = np.sqrt(mean_squared_error(output_test, y_pred))\n",
    "print(\"The test RMSE is: \", round(rmse_test), 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Communicate Results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Equation of My Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features len:  14\n",
      "Index(['TempHighF', 'TempAvgF', 'TempLowF', 'DewPointAvgF',\n",
      "       'HumidityHighPercent', 'HumidityAvgPercent', 'HumidityLowPercent',\n",
      "       'SeaLevelPressureAvgInches', 'VisibilityHighMiles',\n",
      "       'VisibilityAvgMiles', 'VisibilityLowMiles', 'WindHighMPH', 'WindAvgMPH',\n",
      "       'WindGustMPH'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TempHighF                    0.25\n",
       "TempAvgF                    -0.34\n",
       "TempLowF                     0.32\n",
       "DewPointAvgF                -0.22\n",
       "HumidityHighPercent          0.13\n",
       "HumidityAvgPercent          -0.25\n",
       "HumidityLowPercent           0.27\n",
       "SeaLevelPressureAvgInches   -0.00\n",
       "VisibilityHighMiles          0.03\n",
       "VisibilityAvgMiles          -0.11\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the intercept and coefficient from the linear model:\n",
    "intercept = model.intercept_.round(2)\n",
    "coefficients = model.coef_.round(2)\n",
    "# Get the feature names:\n",
    "feature_names = input_ds.columns\n",
    "print(\"features len: \", len(feature_names))\n",
    "print(feature_names)\n",
    "\n",
    "# Put together the coefficients and feature names into a Series:\n",
    "feature_imp = pd.Series(coefficients, index = feature_names)\n",
    "feature_imp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precipation Level  = 0.09\n",
      "+ (0.25 * TempHighF)\n",
      "+ (-0.34 * TempAvgF)\n",
      "+ (0.32 * TempLowF)\n",
      "+ (-0.22 * DewPointAvgF)\n",
      "+ (0.13 * HumidityHighPercent)\n",
      "+ (-0.25 * HumidityAvgPercent)\n",
      "+ (0.27 * HumidityLowPercent)\n",
      "+ (-0.0 * SeaLevelPressureAvgInches)\n",
      "+ (0.03 * VisibilityHighMiles)\n",
      "+ (-0.11 * VisibilityAvgMiles)\n",
      "+ (-0.02 * VisibilityLowMiles)\n",
      "+ (0.08 * WindHighMPH)\n",
      "+ (-0.07 * WindAvgMPH)\n",
      "+ (0.03 * WindGustMPH)\n"
     ]
    }
   ],
   "source": [
    "# Print the linear equation for the model:\n",
    "print(f\"Precipation Level  = {intercept.round(2)}\")\n",
    "for f, c in feature_imp.items():\n",
    "    print(f\"+ ({round(c, 2)} * {f})\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Coefficients for The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a horizontal bar chart to show the top coefficients for my model, based on their absolute value\n",
    "\n",
    "# Sort the coefficients in decending order:\n",
    "feature_imp.sort_values()\n",
    "# Sort them according to their absolute value:\n",
    "feature_imp.sort_values(key=abs).tail(14).plot(kind=\"barh\")\n",
    "plt.xlabel(\"Importance [Inches]\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.title(\"Feature Importance of Precipitation Amount\");\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Predictions (Random Forest Regressor Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>-0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>-0.0110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.1294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>-0.0015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.19</td>\n",
       "      <td>0.7370</td>\n",
       "      <td>0.4530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>-0.0063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.1366</td>\n",
       "      <td>-0.0266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y_test  y_pred   error\n",
       "0    0.00  0.0016 -0.0016\n",
       "1    0.00  0.0000  0.0000\n",
       "2    0.00  0.0110 -0.0110\n",
       "3    0.00  0.0000  0.0000\n",
       "4    0.13  0.0006  0.1294\n",
       "5    0.00  0.0015 -0.0015\n",
       "6    1.19  0.7370  0.4530\n",
       "7    0.00  0.0000  0.0000\n",
       "8    0.00  0.0063 -0.0063\n",
       "9    0.11  0.1366 -0.0266"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame to hold the true values of the test set and the model's predictions:\n",
    "predictions = pd.DataFrame (\n",
    "    zip(output_test, y_pred, output_test - y_pred),\n",
    "    columns = [\"y_test\", \"y_pred\", \"error\"]\n",
    ")\n",
    "\n",
    "predictions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Line plot to visualize model results:\n",
    "\n",
    "plt.figure(figsize = (10, 10))\n",
    "x = np.linspace(0, 5, 5)\n",
    "plt.plot(predictions[\"y_test\"], predictions[\"y_pred\"], 'b.')\n",
    "plt.plot(x, x, 'r-')\n",
    "plt.xlim(0, 4)\n",
    "plt.ylim(0, 4)\n",
    "plt.title(\"Model Predictions\", fontsize=16)\n",
    "plt.xlabel(\"Actual Value\", fontsize=14)\n",
    "plt.ylabel(\"Predicted Value\", fontsize=14)\n",
    "plt.savefig(\"Rainfall Predictions.png\")\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare The Coefficients of The Ridge & Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2d0db10dc30>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ridge.coef_, 's', label = \"Ridge alpha=1\")\n",
    "plt.plot(ridge25.coef_, '^', label = \"Ridge alpha=25\")\n",
    "\n",
    "plt.plot(model.coef_, 'o', label = \"LinearRegression\")\n",
    "plt.xlabel(\"Coefficient Index\")\n",
    "plt.ylabel(\"Coefficient Magnitude\")\n",
    "plt.hlines(0, 0, len(model.coef_))\n",
    "plt.ylim(-0.5, 0.5)\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}