ElNino_RegressionAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oyMOA1a4XYDF"
   },
   "source": [
    "Elnino - Regression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 230205,
     "status": "ok",
     "timestamp": 1669751288909,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "W9DMeqSR8YkR",
    "outputId": "df3a7171-1f32-4891-ad26-7a98ec699dea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 1096,
     "status": "ok",
     "timestamp": 1669751293253,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "UCrZje6o8yjw"
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "executionInfo": {
     "elapsed": 15031,
     "status": "ok",
     "timestamp": 1669751319360,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "NANO6i3s82y1",
    "outputId": "e5f88f03-0d4f-43da-f494-0a7fa2bfe0d4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-682fb579-d214-4c23-8c28-7b5f8b8399b2\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>year_month_day</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>zon_winds</th>\n",
       "      <th>mer_winds</th>\n",
       "      <th>humidity</th>\n",
       "      <th>air_temp_</th>\n",
       "      <th>s_s_temp_</th>\n",
       "      <th>air_temp_F</th>\n",
       "      <th>zon_winds_mph</th>\n",
       "      <th>mer_winds_mph</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-03-07</td>\n",
       "      <td>800307</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.14</td>\n",
       "      <td>26.24</td>\n",
       "      <td>79.052</td>\n",
       "      <td>-15.2116</td>\n",
       "      <td>1.5659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-03-08</td>\n",
       "      <td>800308</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.66</td>\n",
       "      <td>25.97</td>\n",
       "      <td>78.188</td>\n",
       "      <td>-10.9613</td>\n",
       "      <td>2.4607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-09</td>\n",
       "      <td>800309</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.69</td>\n",
       "      <td>25.28</td>\n",
       "      <td>78.242</td>\n",
       "      <td>-10.0665</td>\n",
       "      <td>4.9214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-03-10</td>\n",
       "      <td>800310</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.57</td>\n",
       "      <td>24.31</td>\n",
       "      <td>78.026</td>\n",
       "      <td>-8.5006</td>\n",
       "      <td>4.2503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-03-11</td>\n",
       "      <td>800311</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.30</td>\n",
       "      <td>23.19</td>\n",
       "      <td>77.540</td>\n",
       "      <td>-9.3954</td>\n",
       "      <td>3.3555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178075</th>\n",
       "      <td>1998-06-11</td>\n",
       "      <td>980611</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>94.1</td>\n",
       "      <td>26.04</td>\n",
       "      <td>28.14</td>\n",
       "      <td>78.872</td>\n",
       "      <td>-11.4087</td>\n",
       "      <td>-0.8948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178076</th>\n",
       "      <td>1998-06-12</td>\n",
       "      <td>980612</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.32</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>25.80</td>\n",
       "      <td>27.87</td>\n",
       "      <td>78.440</td>\n",
       "      <td>-9.6191</td>\n",
       "      <td>-7.3821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178077</th>\n",
       "      <td>1998-06-13</td>\n",
       "      <td>980613</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.34</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.17</td>\n",
       "      <td>27.93</td>\n",
       "      <td>80.906</td>\n",
       "      <td>-13.6457</td>\n",
       "      <td>-10.7376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178078</th>\n",
       "      <td>1998-06-14</td>\n",
       "      <td>980614</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>27.36</td>\n",
       "      <td>28.03</td>\n",
       "      <td>81.248</td>\n",
       "      <td>-10.9613</td>\n",
       "      <td>-5.1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178079</th>\n",
       "      <td>1998-06-15</td>\n",
       "      <td>980615</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.09</td>\n",
       "      <td>28.09</td>\n",
       "      <td>80.762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178080 rows × 12 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-682fb579-d214-4c23-8c28-7b5f8b8399b2')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-682fb579-d214-4c23-8c28-7b5f8b8399b2 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-682fb579-d214-4c23-8c28-7b5f8b8399b2');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       year_month_day    date  latitude  longitude  zon_winds  mer_winds  \\\n",
       "0          1980-03-07  800307     -0.02    -109.46       -6.8        0.7   \n",
       "1          1980-03-08  800308     -0.02    -109.46       -4.9        1.1   \n",
       "2          1980-03-09  800309     -0.02    -109.46       -4.5        2.2   \n",
       "3          1980-03-10  800310     -0.02    -109.46       -3.8        1.9   \n",
       "4          1980-03-11  800311     -0.02    -109.46       -4.2        1.5   \n",
       "...               ...     ...       ...        ...        ...        ...   \n",
       "178075     1998-06-11  980611      8.96    -140.33       -5.1       -0.4   \n",
       "178076     1998-06-12  980612      8.96    -140.32       -4.3       -3.3   \n",
       "178077     1998-06-13  980613      8.95    -140.34       -6.1       -4.8   \n",
       "178078     1998-06-14  980614      8.96    -140.33       -4.9       -2.3   \n",
       "178079     1998-06-15  980615      8.95    -140.33        NaN        NaN   \n",
       "\n",
       "        humidity  air_temp_  s_s_temp_  air_temp_F  zon_winds_mph  \\\n",
       "0            NaN      26.14      26.24      79.052       -15.2116   \n",
       "1            NaN      25.66      25.97      78.188       -10.9613   \n",
       "2            NaN      25.69      25.28      78.242       -10.0665   \n",
       "3            NaN      25.57      24.31      78.026        -8.5006   \n",
       "4            NaN      25.30      23.19      77.540        -9.3954   \n",
       "...          ...        ...        ...         ...            ...   \n",
       "178075      94.1      26.04      28.14      78.872       -11.4087   \n",
       "178076      93.2      25.80      27.87      78.440        -9.6191   \n",
       "178077      81.3      27.17      27.93      80.906       -13.6457   \n",
       "178078      76.2      27.36      28.03      81.248       -10.9613   \n",
       "178079       NaN      27.09      28.09      80.762            NaN   \n",
       "\n",
       "        mer_winds_mph  \n",
       "0              1.5659  \n",
       "1              2.4607  \n",
       "2              4.9214  \n",
       "3              4.2503  \n",
       "4              3.3555  \n",
       "...               ...  \n",
       "178075        -0.8948  \n",
       "178076        -7.3821  \n",
       "178077       -10.7376  \n",
       "178078        -5.1451  \n",
       "178079            NaN  \n",
       "\n",
       "[178080 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = '''obs\n",
    "year\n",
    "month\n",
    "day\n",
    "date\n",
    "latitude\n",
    "longitude\n",
    "zon.winds\n",
    "mer.winds\n",
    "humidity\n",
    "air temp.\n",
    "s.s.temp.'''.split('\\n')\n",
    "\n",
    "nino = pd.read_csv('/content/gdrive/tao-all2.dat', sep=' ', names=names, na_values='.', \n",
    "                   parse_dates=[[1,2,3]])\n",
    "\n",
    "def clean_cols(val):\n",
    "    return val.replace('.', '_').replace(' ', '_')\n",
    "\n",
    "nino = (nino\n",
    "  .rename(columns=clean_cols)\n",
    "  .assign(air_temp_F=lambda df_: df_.air_temp_ * 9/5 + 32,\n",
    "        zon_winds_mph=lambda df_: df_.zon_winds*2.237,\n",
    "        mer_winds_mph=lambda df_: df_.mer_winds*2.237)\n",
    "  .drop(columns='obs')\n",
    ")\n",
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 136,
     "status": "ok",
     "timestamp": 1669751513912,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "SdiOiPMB8uEd",
    "outputId": "942f51de-1a25-4e7a-ae8d-92b2973ec03f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-aac1b4ef-af21-496c-82e1-9c89c4df2d4a\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>year_month_day</th>\n",
       "      <th>date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>zon_winds</th>\n",
       "      <th>mer_winds</th>\n",
       "      <th>humidity</th>\n",
       "      <th>air_temp_</th>\n",
       "      <th>s_s_temp_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-03-07</td>\n",
       "      <td>800307</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.14</td>\n",
       "      <td>26.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-03-08</td>\n",
       "      <td>800308</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.66</td>\n",
       "      <td>25.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-09</td>\n",
       "      <td>800309</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.69</td>\n",
       "      <td>25.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-03-10</td>\n",
       "      <td>800310</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.57</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-03-11</td>\n",
       "      <td>800311</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.30</td>\n",
       "      <td>23.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178075</th>\n",
       "      <td>1998-06-11</td>\n",
       "      <td>980611</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>94.1</td>\n",
       "      <td>26.04</td>\n",
       "      <td>28.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178076</th>\n",
       "      <td>1998-06-12</td>\n",
       "      <td>980612</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.32</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>25.80</td>\n",
       "      <td>27.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178077</th>\n",
       "      <td>1998-06-13</td>\n",
       "      <td>980613</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.34</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.17</td>\n",
       "      <td>27.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178078</th>\n",
       "      <td>1998-06-14</td>\n",
       "      <td>980614</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>27.36</td>\n",
       "      <td>28.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178079</th>\n",
       "      <td>1998-06-15</td>\n",
       "      <td>980615</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.09</td>\n",
       "      <td>28.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178080 rows × 9 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aac1b4ef-af21-496c-82e1-9c89c4df2d4a')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-aac1b4ef-af21-496c-82e1-9c89c4df2d4a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-aac1b4ef-af21-496c-82e1-9c89c4df2d4a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       year_month_day    date  latitude  longitude  zon_winds  mer_winds  \\\n",
       "0          1980-03-07  800307     -0.02    -109.46       -6.8        0.7   \n",
       "1          1980-03-08  800308     -0.02    -109.46       -4.9        1.1   \n",
       "2          1980-03-09  800309     -0.02    -109.46       -4.5        2.2   \n",
       "3          1980-03-10  800310     -0.02    -109.46       -3.8        1.9   \n",
       "4          1980-03-11  800311     -0.02    -109.46       -4.2        1.5   \n",
       "...               ...     ...       ...        ...        ...        ...   \n",
       "178075     1998-06-11  980611      8.96    -140.33       -5.1       -0.4   \n",
       "178076     1998-06-12  980612      8.96    -140.32       -4.3       -3.3   \n",
       "178077     1998-06-13  980613      8.95    -140.34       -6.1       -4.8   \n",
       "178078     1998-06-14  980614      8.96    -140.33       -4.9       -2.3   \n",
       "178079     1998-06-15  980615      8.95    -140.33        NaN        NaN   \n",
       "\n",
       "        humidity  air_temp_  s_s_temp_  \n",
       "0            NaN      26.14      26.24  \n",
       "1            NaN      25.66      25.97  \n",
       "2            NaN      25.69      25.28  \n",
       "3            NaN      25.57      24.31  \n",
       "4            NaN      25.30      23.19  \n",
       "...          ...        ...        ...  \n",
       "178075      94.1      26.04      28.14  \n",
       "178076      93.2      25.80      27.87  \n",
       "178077      81.3      27.17      27.93  \n",
       "178078      76.2      27.36      28.03  \n",
       "178079       NaN      27.09      28.09  \n",
       "\n",
       "[178080 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reorder and remove duplicate columns - place 'sea_surface_temp' to the last\n",
    "nino=nino[['year_month_day', 'date', 'latitude', 'longitude', 'zon_winds',\n",
    "       'mer_winds', 'humidity', 'air_temp_', 's_s_temp_']]\n",
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "executionInfo": {
     "elapsed": 163,
     "status": "ok",
     "timestamp": 1669751517107,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "ofRRtsOu89aF",
    "outputId": "1bc2e244-c8ca-4c53-f65b-af531b526692"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:5047: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-cdf6d53d-fe61-4034-b438-e3c6155e45c5\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Year_Month_Day</th>\n",
       "      <th>Date</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Zonal Winds</th>\n",
       "      <th>Meridional Winds</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Air Temp</th>\n",
       "      <th>Sea Surface Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-03-07</td>\n",
       "      <td>800307</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.14</td>\n",
       "      <td>26.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-03-08</td>\n",
       "      <td>800308</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.66</td>\n",
       "      <td>25.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-03-09</td>\n",
       "      <td>800309</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.69</td>\n",
       "      <td>25.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-03-10</td>\n",
       "      <td>800310</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.57</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-03-11</td>\n",
       "      <td>800311</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.30</td>\n",
       "      <td>23.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178075</th>\n",
       "      <td>1998-06-11</td>\n",
       "      <td>980611</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>94.1</td>\n",
       "      <td>26.04</td>\n",
       "      <td>28.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178076</th>\n",
       "      <td>1998-06-12</td>\n",
       "      <td>980612</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.32</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>25.80</td>\n",
       "      <td>27.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178077</th>\n",
       "      <td>1998-06-13</td>\n",
       "      <td>980613</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.34</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.17</td>\n",
       "      <td>27.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178078</th>\n",
       "      <td>1998-06-14</td>\n",
       "      <td>980614</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>27.36</td>\n",
       "      <td>28.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178079</th>\n",
       "      <td>1998-06-15</td>\n",
       "      <td>980615</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.09</td>\n",
       "      <td>28.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178080 rows × 9 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdf6d53d-fe61-4034-b438-e3c6155e45c5')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-cdf6d53d-fe61-4034-b438-e3c6155e45c5 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-cdf6d53d-fe61-4034-b438-e3c6155e45c5');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       Year_Month_Day    Date  Latitude  Longitude  Zonal Winds  \\\n",
       "0          1980-03-07  800307     -0.02    -109.46         -6.8   \n",
       "1          1980-03-08  800308     -0.02    -109.46         -4.9   \n",
       "2          1980-03-09  800309     -0.02    -109.46         -4.5   \n",
       "3          1980-03-10  800310     -0.02    -109.46         -3.8   \n",
       "4          1980-03-11  800311     -0.02    -109.46         -4.2   \n",
       "...               ...     ...       ...        ...          ...   \n",
       "178075     1998-06-11  980611      8.96    -140.33         -5.1   \n",
       "178076     1998-06-12  980612      8.96    -140.32         -4.3   \n",
       "178077     1998-06-13  980613      8.95    -140.34         -6.1   \n",
       "178078     1998-06-14  980614      8.96    -140.33         -4.9   \n",
       "178079     1998-06-15  980615      8.95    -140.33          NaN   \n",
       "\n",
       "        Meridional Winds  Humidity  Air Temp  Sea Surface Temp  \n",
       "0                    0.7       NaN     26.14             26.24  \n",
       "1                    1.1       NaN     25.66             25.97  \n",
       "2                    2.2       NaN     25.69             25.28  \n",
       "3                    1.9       NaN     25.57             24.31  \n",
       "4                    1.5       NaN     25.30             23.19  \n",
       "...                  ...       ...       ...               ...  \n",
       "178075              -0.4      94.1     26.04             28.14  \n",
       "178076              -3.3      93.2     25.80             27.87  \n",
       "178077              -4.8      81.3     27.17             27.93  \n",
       "178078              -2.3      76.2     27.36             28.03  \n",
       "178079               NaN       NaN     27.09             28.09  \n",
       "\n",
       "[178080 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename specific column names\n",
    "\n",
    "nino.rename(columns={'year_month_day': 'Year_Month_Day', 'date': 'Date','latitude': 'Latitude',\n",
    "                     'longitude': 'Longitude', 'zon_winds': 'Zonal Winds', 'mer_winds': 'Meridional Winds', \n",
    "                     'humidity': 'Humidity','air_temp_':'Air Temp', 's_s_temp_': 'Sea Surface Temp'}, inplace=True)\n",
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 163,
     "status": "ok",
     "timestamp": 1669751520544,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "Rmn1wNHNo5xH",
    "outputId": "1450b453-293d-4775-e16d-008efbb501c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178080 entries, 0 to 178079\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count   Dtype         \n",
      "---  ------            --------------   -----         \n",
      " 0   Year_Month_Day    178080 non-null  datetime64[ns]\n",
      " 1   Date              178080 non-null  int64         \n",
      " 2   Latitude          178080 non-null  float64       \n",
      " 3   Longitude         178080 non-null  float64       \n",
      " 4   Zonal Winds       152917 non-null  float64       \n",
      " 5   Meridional Winds  152918 non-null  float64       \n",
      " 6   Humidity          112319 non-null  float64       \n",
      " 7   Air Temp          159843 non-null  float64       \n",
      " 8   Sea Surface Temp  161073 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(7), int64(1)\n",
      "memory usage: 12.2 MB\n"
     ]
    }
   ],
   "source": [
    "nino.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xO4VfjRdphs4"
   },
   "outputs": [],
   "source": [
    "# Changing features to numerical \n",
    "nino['Zonal Winds'] = pd.to_numeric(nino['Zonal Winds'], errors='coerce')\n",
    "nino['Meridional Winds'] = pd.to_numeric(nino['Meridional Winds'], errors='coerce')\n",
    "nino['Humidity'] = pd.to_numeric(nino['Humidity'], errors='coerce')\n",
    "nino['Air Temp'] = pd.to_numeric(nino['Air Temp'], errors='coerce')\n",
    "nino['Sea Surface Temp'] = pd.to_numeric(nino['Sea Surface Temp'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 137,
     "status": "ok",
     "timestamp": 1669751526317,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "4t81WxATpsJ8",
    "outputId": "634faf0c-e85d-4ea8-9d58-671924a3d997"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178080 entries, 0 to 178079\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count   Dtype         \n",
      "---  ------            --------------   -----         \n",
      " 0   Year_Month_Day    178080 non-null  datetime64[ns]\n",
      " 1   Date              178080 non-null  int64         \n",
      " 2   Latitude          178080 non-null  float64       \n",
      " 3   Longitude         178080 non-null  float64       \n",
      " 4   Zonal Winds       152917 non-null  float64       \n",
      " 5   Meridional Winds  152918 non-null  float64       \n",
      " 6   Humidity          112319 non-null  float64       \n",
      " 7   Air Temp          159843 non-null  float64       \n",
      " 8   Sea Surface Temp  161073 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(7), int64(1)\n",
      "memory usage: 12.2 MB\n"
     ]
    }
   ],
   "source": [
    "nino.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 600
    },
    "executionInfo": {
     "elapsed": 1057,
     "status": "ok",
     "timestamp": 1669751529416,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "HUOD2oTe9_5J",
    "outputId": "5cd9ecc5-dc46-486c-9723-a894ebd0cc45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f050fe1ff50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the number of missing values using visualization\n",
    "import missingno as msno\n",
    "\n",
    "msno.bar(nino)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 124,
     "status": "ok",
     "timestamp": 1669751532406,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "iaIK5vMx9xhz",
    "outputId": "7a363f5f-f502-4a91-d7e8-4942807eafcf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5d03f101-ebe0-497b-b72e-c1b0341445b6\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Year_Month_Day</th>\n",
       "      <th>Date</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Zonal Winds</th>\n",
       "      <th>Meridional Winds</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Air Temp</th>\n",
       "      <th>Sea Surface Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4059</th>\n",
       "      <td>1993-05-09</td>\n",
       "      <td>930509</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>81.2</td>\n",
       "      <td>26.80</td>\n",
       "      <td>27.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060</th>\n",
       "      <td>1993-05-10</td>\n",
       "      <td>930510</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>84.2</td>\n",
       "      <td>26.95</td>\n",
       "      <td>26.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>1993-05-11</td>\n",
       "      <td>930511</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.9</td>\n",
       "      <td>26.98</td>\n",
       "      <td>26.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>1993-05-12</td>\n",
       "      <td>930512</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>86.9</td>\n",
       "      <td>26.93</td>\n",
       "      <td>26.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>1993-05-13</td>\n",
       "      <td>930513</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>87.6</td>\n",
       "      <td>27.01</td>\n",
       "      <td>26.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178074</th>\n",
       "      <td>1998-06-10</td>\n",
       "      <td>980610</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.52</td>\n",
       "      <td>28.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178075</th>\n",
       "      <td>1998-06-11</td>\n",
       "      <td>980611</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>94.1</td>\n",
       "      <td>26.04</td>\n",
       "      <td>28.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178076</th>\n",
       "      <td>1998-06-12</td>\n",
       "      <td>980612</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.32</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>25.80</td>\n",
       "      <td>27.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178077</th>\n",
       "      <td>1998-06-13</td>\n",
       "      <td>980613</td>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.34</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.17</td>\n",
       "      <td>27.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178078</th>\n",
       "      <td>1998-06-14</td>\n",
       "      <td>980614</td>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>27.36</td>\n",
       "      <td>28.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93935 rows × 9 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d03f101-ebe0-497b-b72e-c1b0341445b6')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-5d03f101-ebe0-497b-b72e-c1b0341445b6 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-5d03f101-ebe0-497b-b72e-c1b0341445b6');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       Year_Month_Day    Date  Latitude  Longitude  Zonal Winds  \\\n",
       "4059       1993-05-09  930509     -0.02    -109.96         -2.1   \n",
       "4060       1993-05-10  930510     -0.02    -109.96         -3.4   \n",
       "4061       1993-05-11  930511     -0.02    -109.96         -3.8   \n",
       "4062       1993-05-12  930512     -0.02    -109.96         -3.0   \n",
       "4063       1993-05-13  930513     -0.02    -109.96         -4.5   \n",
       "...               ...     ...       ...        ...          ...   \n",
       "178074     1998-06-10  980610      8.95    -140.33         -6.8   \n",
       "178075     1998-06-11  980611      8.96    -140.33         -5.1   \n",
       "178076     1998-06-12  980612      8.96    -140.32         -4.3   \n",
       "178077     1998-06-13  980613      8.95    -140.34         -6.1   \n",
       "178078     1998-06-14  980614      8.96    -140.33         -4.9   \n",
       "\n",
       "        Meridional Winds  Humidity  Air Temp  Sea Surface Temp  \n",
       "4059                 2.1      81.2     26.80             27.02  \n",
       "4060                 1.4      84.2     26.95             26.91  \n",
       "4061                 2.2      84.9     26.98             26.78  \n",
       "4062                 1.5      86.9     26.93             26.74  \n",
       "4063                 1.9      87.6     27.01             26.82  \n",
       "...                  ...       ...       ...               ...  \n",
       "178074              -5.3      81.3     27.52             28.17  \n",
       "178075              -0.4      94.1     26.04             28.14  \n",
       "178076              -3.3      93.2     25.80             27.87  \n",
       "178077              -4.8      81.3     27.17             27.93  \n",
       "178078              -2.3      76.2     27.36             28.03  \n",
       "\n",
       "[93935 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove rows that contain missing values\n",
    "nino = nino.dropna(axis=0)\n",
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 139,
     "status": "ok",
     "timestamp": 1669751535583,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "70syCs0l-GTK",
    "outputId": "e8cc8e91-e180-4ecc-e6c7-8dc830b8d5be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                0.052066\n",
       "Latitude            0.125119\n",
       "Longitude           0.304027\n",
       "Zonal Winds         0.376015\n",
       "Meridional Winds   -0.284897\n",
       "Humidity           -0.324348\n",
       "Air Temp            0.940233\n",
       "Sea Surface Temp    1.000000\n",
       "Name: Sea Surface Temp, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlate sea surface temp with other variables\n",
    "nino.corr()['Sea Surface Temp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "executionInfo": {
     "elapsed": 998,
     "status": "ok",
     "timestamp": 1669751538725,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "mVMnOVR89hqT",
    "outputId": "a4170fb0-a383-4ef2-df93-bb8f1a05e631"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f050fc4aad0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.scatterplot('Longitude', 'Latitude', data=nino, color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 146,
     "status": "ok",
     "timestamp": 1669751541963,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "E9MnYM7drt2r",
    "outputId": "d9befdf7-a43f-4722-a18b-7615ae2f77ee"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# Drop year-month-day and date columns from dataset\n",
    "nino.drop(columns=['Year_Month_Day', 'Date'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 147,
     "status": "ok",
     "timestamp": 1669751544058,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "2F_8CCPetUeO",
    "outputId": "63007401-37f4-45bf-d0fd-47f304ac0859"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-984bb707-d71c-48d1-b3f8-0a51e7046e74\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Zonal Winds</th>\n",
       "      <th>Meridional Winds</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Air Temp</th>\n",
       "      <th>Sea Surface Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4059</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>81.2</td>\n",
       "      <td>26.80</td>\n",
       "      <td>27.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>84.2</td>\n",
       "      <td>26.95</td>\n",
       "      <td>26.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4061</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.9</td>\n",
       "      <td>26.98</td>\n",
       "      <td>26.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4062</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>86.9</td>\n",
       "      <td>26.93</td>\n",
       "      <td>26.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.96</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>87.6</td>\n",
       "      <td>27.01</td>\n",
       "      <td>26.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178074</th>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.52</td>\n",
       "      <td>28.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178075</th>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-5.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>94.1</td>\n",
       "      <td>26.04</td>\n",
       "      <td>28.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178076</th>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.32</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>93.2</td>\n",
       "      <td>25.80</td>\n",
       "      <td>27.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178077</th>\n",
       "      <td>8.95</td>\n",
       "      <td>-140.34</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>81.3</td>\n",
       "      <td>27.17</td>\n",
       "      <td>27.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178078</th>\n",
       "      <td>8.96</td>\n",
       "      <td>-140.33</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>76.2</td>\n",
       "      <td>27.36</td>\n",
       "      <td>28.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93935 rows × 7 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-984bb707-d71c-48d1-b3f8-0a51e7046e74')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-984bb707-d71c-48d1-b3f8-0a51e7046e74 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-984bb707-d71c-48d1-b3f8-0a51e7046e74');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        Latitude  Longitude  Zonal Winds  Meridional Winds  Humidity  \\\n",
       "4059       -0.02    -109.96         -2.1               2.1      81.2   \n",
       "4060       -0.02    -109.96         -3.4               1.4      84.2   \n",
       "4061       -0.02    -109.96         -3.8               2.2      84.9   \n",
       "4062       -0.02    -109.96         -3.0               1.5      86.9   \n",
       "4063       -0.02    -109.96         -4.5               1.9      87.6   \n",
       "...          ...        ...          ...               ...       ...   \n",
       "178074      8.95    -140.33         -6.8              -5.3      81.3   \n",
       "178075      8.96    -140.33         -5.1              -0.4      94.1   \n",
       "178076      8.96    -140.32         -4.3              -3.3      93.2   \n",
       "178077      8.95    -140.34         -6.1              -4.8      81.3   \n",
       "178078      8.96    -140.33         -4.9              -2.3      76.2   \n",
       "\n",
       "        Air Temp  Sea Surface Temp  \n",
       "4059       26.80             27.02  \n",
       "4060       26.95             26.91  \n",
       "4061       26.98             26.78  \n",
       "4062       26.93             26.74  \n",
       "4063       27.01             26.82  \n",
       "...          ...               ...  \n",
       "178074     27.52             28.17  \n",
       "178075     26.04             28.14  \n",
       "178076     25.80             27.87  \n",
       "178077     27.17             27.93  \n",
       "178078     27.36             28.03  \n",
       "\n",
       "[93935 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w56qguf6jdZa"
   },
   "source": [
    "Predictive Models - Linear Regression, Ridge Regression and Knn and Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 279,
     "status": "ok",
     "timestamp": 1669751547075,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "JZDBl1B_nmsa"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x0vS56gGn0se"
   },
   "outputs": [],
   "source": [
    "X=nino.drop('Sea Surface Temp', axis=1)\n",
    "y=nino['Sea Surface Temp']\n",
    "\n",
    "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 122,
     "status": "ok",
     "timestamp": 1669751555812,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "hQBvpBJaoL7Z"
   },
   "outputs": [],
   "source": [
    "# Define the a function, that can be called for all models\n",
    "def Predictive_Model(estimator):\n",
    "    estimator.fit(train_X, train_y)\n",
    "    prediction = estimator.predict(test_X)\n",
    "    print('R_squared:', metrics.r2_score(test_y, prediction))\n",
    "    print('Square Root of MSE:',np.sqrt(metrics.mean_squared_error(test_y, prediction)))\n",
    "    plt.figure(figsize=(10,5))\n",
    "    sns.distplot(test_y, hist=True, kde=False)\n",
    "    sns.distplot(prediction, hist=True, kde=False)\n",
    "    plt.legend(labels=['Actual Values of Price', 'Predicted Values of Price'])\n",
    "    plt.xlim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 397,
     "status": "ok",
     "timestamp": 1669751559409,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "Y1r-kIXRoSk5"
   },
   "outputs": [],
   "source": [
    "# Function that defines magnitude of each coefficient\n",
    "def FeatureBar(model_Features, Title, yLabel):\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.bar(nino.columns[nino.columns!='Sea Surface Temp'].values, model_Features)\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.title(Title)\n",
    "    plt.ylabel(yLabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2gBZp7T5oVGd"
   },
   "source": [
    "Basic Prediction - Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "executionInfo": {
     "elapsed": 870,
     "status": "ok",
     "timestamp": 1669751562297,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "AMNdViXAoTjI",
    "outputId": "df953b0b-9275-42dd-8124-8123596788cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared: 0.9120690361149137\n",
      "Square Root of MSE: 0.5524855619133298\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "Predictive_Model(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q2dRUchCtzpA"
   },
   "source": [
    "The first one is the coefficient of determination, which is usually expressed as R². The coefficient of determination is the ratio of the variance of a target explained or predicted by a model over the total variance of the target. It ranges from 0 to 1, and as the value is closer to 1, the model explains or predicts the variance of the target better.\n",
    "\n",
    "The second metric is the Mean Squared Error(MSE). The MSE is the average of the squared difference between the estimated or predicted values and the actual values of a target. This is always greater than zero. A lower value of the MSE indicates higher accuracy of predictions of a model. In this analysis, I use the square root of this metric(RMSE)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6REtumTJt1Hd"
   },
   "source": [
    "Linear regression - R² is 0.91 which means 91% of the sea surface temp can be predicted by the model. \n",
    "\n",
    "The RMSE is 0.55. This means that for all the predictions for the testing set, the average difference for each prediction is 0.55."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XtsjfvqoucEt"
   },
   "source": [
    "Each coefficient of the linear regression represents the magnitude of the impact of the feature on the target. The bar chart below shows the magnitudes for each coefficient in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 132,
     "status": "ok",
     "timestamp": 1669751566475,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "asjA3lNQumw0",
    "outputId": "92c258d1-4439-4d0d-cddd-1db500eb90c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.40829770e-02, 2.81570964e-04, 8.27624481e-02, 8.51693124e-03,\n",
       "       4.07947714e-03, 1.01363589e+00])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "executionInfo": {
     "elapsed": 338,
     "status": "ok",
     "timestamp": 1669751568332,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "TWDHAyVlua7r",
    "outputId": "c4b2bfe5-09ee-4906-f3fb-cc779e0000c0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use featurebar function and predict the magnitude of each coefficient in the model\n",
    "FeatureBar(lr.coef_, 'Feature Coefficients', 'Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C3IsJh40vNU-"
   },
   "source": [
    "The coefficients for - longitude, Meridional Winds and Humidity in the model have least impact on the sea surface temperature. \n",
    "\n",
    "Zonal Winds and Air temp have an impact in predicting sea surface temp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hJ8YGTJJwTqg"
   },
   "source": [
    "kNN Regressor with k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 1364,
     "status": "ok",
     "timestamp": 1669751573376,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "fE4hpjsvwXWq",
    "outputId": "7cdb60ba-f09a-4721-a262-039e88daf160"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared: 0.9358772601107423\n",
      "Square Root of MSE: 0.4717979523392589\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn = KNeighborsRegressor(n_neighbors=5)\n",
    "Predictive_Model(knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UgKXHfcbw5E5"
   },
   "source": [
    "The KNN model makes predictions for the target using the target values of K-nearest neighbors. The result above is obtained with the number of the neighbors equal to 5. The R² of this model is 0.935, and the RMSE is 0.47.\n",
    "\n",
    "Compared to the distributions of the predicted values from linear and KNN model seems to predict the sea surface temp better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C_T530rBxONM"
   },
   "source": [
    "Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 1136,
     "status": "ok",
     "timestamp": 1669751578955,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "trBkN1f9w3oM",
    "outputId": "c5690a68-a628-428e-9712-5847920a182a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared: 0.9398155233088793\n",
      "Square Root of MSE: 0.4570800439883308\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAE9CAYAAADeXLzSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXRV1d3/8c/NSEggEyTIoAWFp5QpKogIggYIQoBQBIc6IIWfAyJVXCpDQRsQeFw8DsCjhVoRh0exCEEJKg0qAUFQgVJKGCtDlNxgIPNwp/P7g3LghkBIyMlNwvu1Fmux9z3n3O/d3rX4eM6+e9sMwzAEAAAAy/j5ugAAAICGjsAFAABgMQIXAACAxQhcAAAAFiNwAQAAWIzABQAAYLEAXxdwMdu3b1dISIivy2jQysrKFBwc7OsyGizG13qMsbUYX2sxvtarzTEuKytTXFxcha/V6cBls9nUsWNHX5fRoGVkZDDGFmJ8rccYW4vxtRbja73aHOOMjIwLvsYjRQAAAIsRuAAAACxG4AIAALBYnZ7DVRGn06nMzEyVlpb6upQGwel0XvSZM85q1KiRWrdurcDAQF+XAgCoZ+pd4MrMzFSTJk30q1/9Sjabzdfl1HslJSX8EvQSGIahnJwcZWZmqm3btr4uBwBQz9S7R4qlpaWKjo4mbKFW2Ww2RUdHc2cVAFAt9S5wSSJswSf43gEAqqteBq66IC0tTf/1X/+lQ4cOVXrs22+/rZKSkmq/18qVK5WcnOzVl5mZqb59+8rj8Xj1JyUl6R//+EeF18nMzNTQoUOrXUd1ORwOPfTQQ0pKStLatWu9XpsyZYri4+OVlJSk3/72t9qxY0eF13jttde0efPm2igXAIAaV+/mcJWXV+xQQZmrxq7XJDhA4Y2DKj1uzZo1uvHGG5WamqpJkyZd9Nh33nlHw4cPr9G5Uq1bt1bLli31/fff66abbpIkHTp0SEVFRerWrVuNvU9N2LNnjyRp9erVFb7+7LPP6o477tCmTZs0c+ZMffrpp16vu91u/eEPf7C8TgAArFLvA1dBmUvp+3+psev17dCs0sBVVFSkH374Qe+8844effRRM3C53W7Nnz9fGzdulM1m01133SXDMJSdna0xY8YoIiJC7777rq6//nrzTs7nn3+ur7/+WvPmzdOXX36pN954Q06nUxEREZo/f76aNWt2wToSExOVmppqBq61a9cqMTFRmZmZevbZZ827ajNmzNANN9zgde7KlSu1e/duPfPMM5KkRx55RL///e/Vs2dPbdq0SQsXLpTD4VCbNm00d+5chYaGav78+fryyy/l7++vPn366LnnnvO6Zm5urqZNm6Zjx44pJCREycnJat68uZ555hmdPHlSSUlJWrhwoa6++uoKP0+PHj109OhRSVJ8fLwGDx6szZs3a/z48dq4caNuu+023XHHHdq1a5fmzJmj4uJiBQUF6e2331ZISIjmz5+vbdu2yeFw6L777tM999xz0f+OAADUlnofuHxh/fr1uvXWW9W2bVtFRkZq9+7d6ty5s5YvX66ffvpJKSkpCggIUG5uriIiIvT2229r2bJlioqKuuh1b7zxRn300Uey2Wz629/+pjfffFNTpky54PGDBw/WiBEjNGPGDAUEBGjt2rV67bXXFB0draVLlyo4OFiHDx/W5MmTtXLlykv6bCdPntQbb7yhpUuXqnHjxlqyZImWLl2q++67T3//+9/1+eefy2azKT8//7xzFy5cqN/85jd6/fXXtWXLFj333HNavXq1Zs+erbfeekuLFy++6Ht/+eWX6tChg9mOiIjQqlWrJEkbN26UdPrx5FNPPaVXXnlFXbt2VWFhoRo1aqQVK1aoSZMm+vjjj+VwOHTPPfeod+/eatOmzSV9bgAArETgqobU1FQ9+OCDkqQhQ4YoNTVVnTt31pYtW3TPPfcoIOD0sEZERFTpullZWXrqqad04sQJORwOtW7d+qLHN2vWTO3bt9eWLVvUrFkzBQQEqEOHDiooKFBycrL27t0rPz8/HT58+JJr+Mc//qGDBw/q3nvvlXR6na64uDg1adJEwcHBmjZtmm6//Xbddttt5537ww8/aOHChZKkXr16KTc3V4WFhZW+50svvaQ33nhDUVFRevHFF83+IUOGnHfsjz/+qObNm6tr166SpLCwMEnSN998o3379umLL76QJBUUFOjIkSMELgBoKIpPSY4C776gJlLjSN/UU0UErirKzc3Vt99+q/3798tms8ntdstms+nZZ5+t1vXKysrMv8+ePVsPPfSQ+vfvr61bt2rRokWVnp+YmKi1a9cqOjpaiYmJkk5P0m/WrJlWr14tj8djhpNz+fv7e024P1OHYRjq3bu3Xn755fPOWbFihbZs2aLPP/9c7733nt55550qf96KnJnDVV5V5rwZhqE//vGPuvXWW2ukJgBAHeMokA6u9+67rn+9CVz8SrGKvvjiCyUlJemrr77Sl19+qQ0bNqh169b6/vvvdcstt2j58uVyuU5P4s/NzZUkhYaGqqioyLxGs2bNdOjQIXk8HqWlpZn9BQUFio2NlSSlpKRcUj0JCQnasGGDOX/rzHWaN28uPz8/rV69Wm63+7zzWrVqpb1798rj8ej48ePatWuXJCkuLk7bt2/XkSNHJEnFxcX68ccfVVRUpIKCAvXr10/Tpk3Tvn37zrtm9+7d9cknn0iStm7dqsjISPMOVE1p27atTpw4YdZbWFgol8ulPn366IMPPpDT6ZR0+k5YcXFxjb43AADVxR2uKlqzZo3+3//7f159CQkJWrNmjWbMmKHDhw9r+PDhCggI0F133aX7779fd911l8aPH6+YmBi9++67evrpp/XII48oKipKnTt3NoPBxIkT9Yc//EHh4eHq2bOnMjMzK62nadOmiouL0y+//GI+Pvvd736nJ554QikpKbr11lvVuHHj88678cYb1apVK40cOVLXXXedOnXqJEmKiorS3LlzNXnyZDkcDknSk08+qdDQUE2YMMG8E1bR3LKJEydq2rRpGjZsmEJCQjRv3rwqjOylCQoK0iuvvKLZs2ertLRUjRo10tKlSzV69Gj99NNPGjlypAzDUGRkpF5//fUaf38AAKrDZhiG4esiLmTHjh26/vrrvfoyMjLUsWNHs+2rZSEaCrb2qZry37+aPh5Vxxhbi/G1FuNbBblHK36kGFHxL9/PqM0xvth71fs7XOGNg66ogAQAAOof5nABAABYjMAFAABgMQIXAACAxQhcAAAAFiNwAQAAWIzAVQ0dO3ZUUlKShg4dqkmTJpmbRFfHlClT9Pnnn0uSpk+froMHD17w2K1bt2r79u1Vfo/4+HidPHnSq2/q1Kn68MMPvfrS0tI0fvz4S6q1Nn322WcaPHiwHnjgAa/+zMxMde3aVUlJSRoyZIhmzpzptXr+GXa73dxgHAAAX6j3y0JUuLfS5biEfZkaNWqk1atXS5Kefvppffjhhxo7dqz5usvlMvdTrIpz9xGsyLZt29S4cWPdcMMNVb52eYmJiVq8eLGSkpLMvtTUVA0dOvSyr13TVqxYoVmzZql79+7nvXb11Vdr9erVcrlcGjNmjNLS0pSQkGC+7nK5FBsbqwULFtRmyQAAeKn/gauivZUuRxX3Zerevbv27dunrVu36rXXXlPTpk31448/au3atZo/f762bdsmh8Oh++67T/fcc48Mw9CsWbP0zTff6KqrrlJgYKB5rQceeEDPPvusunTpovT0dL3yyityu92KjIzUiy++qA8//FB+fn765JNPNGPGDLVr107PP/+8fv75Z0nStGnTdOONN+rUqVN6+umnZbfbFRcXp4rWtu3Vq5emTJmiEydO6Oqrr1ZxcbE2b96sWbNmadGiRfrqq69UVlam66+/XsnJybLZbF7nx8fHa8WKFYqKitI///lPvfTSS3r33XdVXFysWbNm6cCBA3K5XJo4caIGDBigAwcOaOrUqXI6nfJ4PFq4cKF+9atfeV1zzZo1Wrx4sQzDUL9+/fTMM89o0aJF2r59u6ZPn674+Hg999xzFf53CAgI0PXXX68jR45o5cqVWrdunYqLi+XxeDRv3jw9+uijWrNmjdxut+bPn6+NGzfKZrPprrvu0gMPPKDdu3dr3rx5Ki4uVmRkpObOnauYmJhL/h4AAHAx9T9w+ZDL5VJ6erq5YfKePXv06aefqk2bNlq+fLmaNGmijz/+WA6HQ/fcc4969+6tjIwMM5D98ssvSkxM1J133ul13ZMnT2rGjBl677331KZNG+Xm5ioiIkL33HOPGjdurHHjxkk6fXdtzJgx6t69u37++WeNGzdOn332mf73f/9XN9xwgyZOnKivv/5aK1asOK92f39/JSQkaN26dRo/fry++uor9ezZU2FhYbr//vs1ceJESdIzzzyjr776SvHx8Zc0Jn/+85918803a+7cucrPz9fo0aN1yy236MMPP9SDDz6o4cOHy+FwnPfoz263a/78+Vq5cqWaNm2q3//+90pLS9PEiRO1detWM4heSElJibZs2aJJkyYpJydHe/bs0SeffKKIiAivLZKWL1+un376SSkpKQoICFBubq6cTqdmz56t119/XVFRUVq7dq1eeeUVzZ0795I+MwAAlSFwVUNpaan5KK579+4aNWqUduzYoS5dupj7GX7zzTfat2+fvvjiC0mnN5Q+cuSIvvvuOyUmJsrf31+xsbG6+eabz7v+zp071b17d/NaERERFdaxefNmrzlfhYWFKioq0nfffadFixZJkm677TaFh4dXeH5iYqLmzZun8ePHKzU11fxMW7du1ZtvvqnS0lLl5uaqffv2lxy4Nm3apC+//FJvvfWWJKmsrEzHjx9XXFyc/vznPysrK0sJCQnn3d365z//qZtuuklRUVGSpGHDhum7777TgAEDLvp+R48eVVJSkmw2m/r3769+/fpp5cqV6t27d4XjtmXLFt1zzz3mI9+IiAjt379f+/fvNx8LezweNW/e/JI+LwAAl4LAVQ3nzuE617mbRBuGoT/+8Y/m3a8zNmzYUGN1eDweffTRRwoODq7W+TfccINOnDihvXv3aseOHXrllVdUVlamP/3pT/r444911VVXaeHCheaG1efy9/c3H1WWf33BggVq166dV9+1116rbt266euvv9bDDz+sP/3pT+rVq1e16j7XmTlc5VVlf0jDMNS+fXstX778susBAKAilf5K8fjx43rggQc0ZMgQJSYmatmyZZKkhQsX6tZbb1VSUpKSkpK8gsTixYs1cOBADRo0SBs3bjT709PTNWjQIA0cOFBLliyx4OPUHX369NEHH3wgp9MpSfrxxx9VXFysHj166LPPPpPb7VZ2dra2bt163rlxcXH6/vvvdezYMUlSbm6uJCk0NFRFRUVe7/Huu++a7YyMDElSjx499Omnn0o6HfDy8vIqrNFms2nQoEF67rnn1LdvXwUHB5vhKTIyUkVFReYduvJatWql3bt3S5LWrVvnVdN7771nhrE9e/ZIko4dO6Y2bdrowQcfVP/+/bVv3z6v63Xt2lXfffedTp48KbfbrdTUVPXo0aPC974ct9xyi5YvXy6X6/SG57m5uWrbtq1OnjypHTt2SJKcTqcOHDhQ4+8NALhyVXqHy9/fX1OmTFGnTp1UWFioO++8U71795YkPfTQQ+Z8ojMOHjyo1NRUpaamym63a+zYseY/2snJyVq6dKliY2M1atQoxcfH67rrrrPgY/ne6NGj9dNPP2nkyJEyDEORkZF6/fXXNXDgQH377bcaMmSIWrZsqbi4uPPOjYqKUnJysp544gl5PB5FR0dr6dKluv322zVp0iStX79eM2bM0PTp05WcnKxhw4bJ7Xare/fuSk5O1uOPP66nn35aiYmJuv7669WyZcsL1nnHHXfo7bff1tNPPy1Jatq0qUaPHq2hQ4eqWbNmF5w3NXHiRE2fPl2vvfaaevbsafZPmDBBc+bM0fDhw+XxeNS6dWstXrxYn332mVavXq2AgAA1a9ZMjzzyiNf1YmJizDlpZybNV/Y4sTpGjx6tw4cPa/jw4QoICNBdd92l+++/XwsWLNDs2bNVUFAgt9utMWPGqH379jX+/gCAK5PNqOgnbBfx2GOP6f7779f27du9JnCfsXjxYkky/0EdN26cOQF70aJF+utf/1rhcRXZsWOHrr/+eq++jIwMdezY8WyHD5aFaEhKSkqq9PjtSnfe96+Gj0fVMcbWYnytxfhWQe7R81cluK6/FHH1RU+rzTG+2HtVaQ5XZmamMjIy1K1bN23fvl3vv/++UlJS1LlzZ02ZMkXh4eGy2+3q1q2beU5sbKzsdrskqUWLFl79u3btqs7n8dY48ooKSAAAoP655MBVVFSkSZMmadq0aQoLC9O9996rCRMmyGaz6bXXXtO8efNq/Gf0Ho/HnJd0htPpvKyV3eHNMAzGswqcTud538mLKS0trdLxqDrG2FqMr7UY30vXPLBUzqwsr77AiBydOF50gTNOqytjfEmBy+l0atKkSRo2bJi5inezZs3M10ePHq1HH31U0uk7V1nnDIjdbldsbKwkXbD/Qvz8/M67NZeRkcEjsBrEI8WqCQwM5JFiHcMYW4vxtRbjWwW5R6VznpRJkppFq1kde6R4IZX+StEwDE2fPl3t2rXz2r4mOzvb/HtaWpo5wTg+Pl6pqalyOBw6duyYDh8+rK5du6pLly46fPiwjh07JofDodTU1Ete26mimoDaxvcOAFBdld7h+uGHH7R69Wp16NDBXBhz8uTJWrNmjfbu3Svp9BIBycnJkqT27dtr8ODBGjJkiPz9/TVz5kz5+/tLkmbOnKnx48fL7XbrzjvvrNavwBo1aqScnBxFR0eft90MYBXDMJSTk6NGjRr5uhQAQD1UaeA6s1dgef369bvgOY899pgee+yxCs+52HmXonXr1srMzNSJEycu6zo4zel0eu3niAtr1KiRWrdu7esyAAD1UL1baT4wMFBt27b1dRkNBvMHAACwXqVzuAAAAHB5CFwAAAAWI3ABAABYjMAFAABgsXo3aR4AAFwByu+V7Cz1XS01gMAFAADqHkeB92bVrXv4rpYawCNFAAAAixG4AAAALEbgAgAAsBhzuAAAQJ1T6vSopNhhtkNcbnkcbjUO8vdhVdVH4AIAAHWOw+XWfnuh2W57lUuBrvobuHikCAAAYDECFwAAgMUIXAAAABYjcAEAAFiMwAUAAGAxAhcAAIDFCFwAAAAWI3ABAABYjMAFAABgMVaaBwAA9ZPbJeUePdsOaiI1jvRdPRdB4AIAAPWC22Po1Dn7K4aVFSnw5+/PHnBdfwIXAADA5XC6DR06UWS2u7XzKNCH9VQFc7gAAAAsRuACAACwGIELAADAYgQuAAAAixG4AAAALEbgAgAAsBiBCwAAwGIELgAAAIsRuAAAACxG4AIAALAYgQsAAMBiBC4AAACLEbgAAAAsRuACAACwGIELAADAYpUGruPHj+uBBx7QkCFDlJiYqGXLlkmScnNzNXbsWCUkJGjs2LHKy8uTJBmGodmzZ2vgwIEaNmyY/vWvf5nXWrVqlRISEpSQkKBVq1ZZ9JEAAADqlkoDl7+/v6ZMmaK1a9dq+fLl+r//+z8dPHhQS5YsUa9evbRu3Tr16tVLS5YskSSlp6fr8OHDWrdunWbNmqUXXnhB0umAtmjRIn300Uf629/+pkWLFpkhDQAAoCGrNHDFxMSoU6dOkqSwsDC1a9dOdrtd69ev14gRIyRJI0aMUFpamiSZ/TabTXFxccrPz1d2drY2bdqk3r17KyIiQuHh4erdu7c2btxo4UcDAACoGwKqcnBmZqYyMjLUrVs35eTkKCYmRpLUvHlz5eTkSJLsdrtatGhhntOiRQvZ7fbz+mNjY2W322viMwAAgHour9ihgjKX2W7sMXxYTc275MBVVFSkSZMmadq0aQoLC/N6zWazyWaz1XhxHo9HGRkZNX5dnFVaWsoYW4jxtR5jbC3G11qM71mOgDCl7fnZbP+ua1Pl5Z+deuRwOuV2OLz6XC6Xjmdlme3AiBydOF7kdd26MsaXFLicTqcmTZqkYcOGKSEhQZIUHR2t7OxsxcTEKDs7W1FRUZJO37nKOufDZ2VlKTY2VrGxsdq2bZvZb7fbddNNN130ff38/NSxY8cqfyhcuoyMDMbYQoyv9RhjazG+1mJ8z8o8VayrWpy9qxUYWKbwpuFmOygwUO6gIK++gIAAXXXO0zM1i1aziKu9rlubY3yxYFfpHC7DMDR9+nS1a9dOY8eONfvj4+OVkpIiSUpJSVH//v29+g3D0M6dO9WkSRPFxMSoT58+2rRpk/Ly8pSXl6dNmzapT58+l/vZAAAA6rxK73D98MMPWr16tTp06KCkpCRJ0uTJk/Xwww/rySef1IoVK9SyZUu9+uqrkqR+/fppw4YNGjhwoEJCQjRnzhxJUkREhCZMmKBRo0ZJkh5//HFFRERY9bkAAADqjEoDV/fu3bVv374KXzuzJte5bDabnn/++QqPHzVqlBm4AAAArhSsNA8AAGCxKi0LAQAAUB3ll31oEhyg8MZBPqyodhG4AACA5QrKXErf/4vZ7tuh2RUVuHikCAAAYDECFwAAgMV4pAgAAGqdy+1R5qlis13mdPuwGusRuAAAQK0rcXq049BJs3391Q17bU4eKQIAAFiMwAUAAGAxHikCAACfi7QVqWtYvtkObmD3hAhcAADAWsWn1LT0pLqGlZhdkbZAr0MCXIUq2vOF2fa7eUDl1zWkU8UOsxni9KjR5VdrCQIXAACwlqNA7gNpKrIXml0teg2XFHxZl3VL2n/ONTu2d9fZwNWw7tcBAADUQQQuAAAAixG4AAAALMYcLgAAUOsa+RkN+leJ5RG4AABArbO5S1S0J81sX9KvEuuxhh0nAQAA6gACFwAAgMUIXAAAABYjcAEAAFiMwAUAAGAxAhcAAIDFCFwAAAAWI3ABAABYjMAFAABgMQIXAACAxQhcAAAAFiNwAQAAWIzABQAAYDECFwAAgMUIXAAAABYjcAEAAFgswNcFAACABqb4lOQoONt2lvquljqCwAUAAGpUaVGeSjK+MNsh7W6W0+3xYUW+R+ACAAA1yuFya7+90Gy3vcqlKzxvMYcLAADAagQuAAAAi1UauKZOnapevXpp6NChZt/ChQt16623KikpSUlJSdqwYYP52uLFizVw4EANGjRIGzduNPvT09M1aNAgDRw4UEuWLKnhjwEAAFB3VTqHa+TIkbr//vv13HPPefU/9NBDGjdunFffwYMHlZqaqtTUVNntdo0dO1ZffHF60lxycrKWLl2q2NhYjRo1SvHx8bruuutq8KMAAADUTZUGrh49eigzM/OSLrZ+/XolJiYqKChIbdq00TXXXKNdu3ZJkq655hq1adNGkpSYmKj169cTuAAAwBWh2nO43n//fQ0bNkxTp05VXl6eJMlut6tFixbmMbGxsbLb7RfsBwAAuBJUa1mIe++9VxMmTJDNZtNrr72mefPmae7cuTVdmzwejzIyMmr8ujirtLSUMbYQ42s9xthajK+1Gur4RgW7lZefZ7YdTqfcDodXn8ftqVL7Uq5RUlKin8qNZ10Z42oFrmbNmpl/Hz16tB599FFJp+9cZWVlma/Z7XbFxsZK0gX7L8bPz08dO3asTom4RBkZGYyxhRhf6zHG1mJ8rdVQxzf/+CGFNw0320GBgXIHBXn1+fn7Val9KdcICQlRx3bXetVSm2N8sWBXrUeK2dnZ5t/T0tLUvn17SVJ8fLxSU1PlcDh07NgxHT58WF27dlWXLl10+PBhHTt2TA6HQ6mpqYqPj6/OWwMAANQ7ld7hmjx5srZt26ZTp06pb9++euKJJ7Rt2zbt3btXktSqVSslJydLktq3b6/BgwdryJAh8vf318yZM+Xv7y9JmjlzpsaPHy+3260777zTDGkAAKCeK7d3YqAcPiymbqo0cL388svn9Y0ePfqCxz/22GN67LHHzuvv16+f+vXrV8XyAABAXVd+78Tgtjf7sJq6ib0UAQDAZSm/d2Lntj4spo5iax8AAACLEbgAAAAsRuACAACwGIELAADAYgQuAAAAixG4AAAALEbgAgAAsBiBCwAAwGIsfAoAABoEjyFlnir26vMLDvVRNd4IXAAAoEFweTxK3/+LV1+nKJuPqvFG4AIAAA1CIz9DXcPyzXaJX2NJgb4r6BwELgAA0CDY3CUq2pNmtkN/M0huhfuworOYNA8AAGAxAhcAAIDFCFwAAAAWI3ABAABYjMAFAABgMQIXAACAxVgWAgAAXFjxKclRcLYd1ERqHOm7euopAhcAALgwR4F0cP3Z9nX9CVzVwCNFAAAAixG4AAAALEbgAgAAsBhzuAAAwAWVOj0qKXaYbf8Sp4ptxXJ5zh7T2GP4oLL6hcAFAAAuyOFya7+90GyHRpfIXy7tOJpr9iW0InBVhsAFAAAuWVSITUFuu7qGlZl9wcxQqhSBCwAAXLIAd6mc+9JVdLLY7PO7eYAPK6ofiKQAAAAWI3ABAABYjMAFAABgMQIXAACAxQhcAAAAFiNwAQAAWIzABQAAYDECFwAAgMUIXAAAABYjcAEAAFiMwAUAAGCxSgPX1KlT1atXLw0dOtTsy83N1dixY5WQkKCxY8cqLy9PkmQYhmbPnq2BAwdq2LBh+te//mWes2rVKiUkJCghIUGrVq2y4KMAAADUTZUGrpEjR+rNN9/06luyZIl69eqldevWqVevXlqyZIkkKT09XYcPH9a6des0a9YsvfDCC5JOB7RFixbpo48+0t/+9jctWrTIDGkAAAANXaWBq0ePHgoPD/fqW79+vUaMGCFJGjFihNLS0rz6bTab4uLilJ+fr+zsbG3atEm9e/dWRESEwsPD1bt3b23cuNGCjwMAAFD3VGsOV05OjmJiYiRJzZs3V05OjiTJbrerRYsW5nEtWrSQ3W4/rz82NlZ2u/1y6gYAAKg3Ai73AjabTTabrSZqOY/H41FGRoYl18ZppaWljLGFGF/rMcbWYnytVR/GNyrYrbz8s9OAHE6n3A6HV5/H7blouzrn1MQ1jKJCBfo1rhNjXK3AFcN6d00AABb5SURBVB0drezsbMXExCg7O1tRUVGSTt+5ysrKMo/LyspSbGysYmNjtW3bNrPfbrfrpptuqvR9/Pz81LFjx+qUiEuUkZHBGFuI8bUeY2wtxtda9WF8848fUnjTs1OLggID5Q4K8urz8/e7aLs659TENUJDw+QOCFTH9u2q9dmr6mLBrlqPFOPj45WSkiJJSklJUf/+/b36DcPQzp071aRJE8XExKhPnz7atGmT8vLylJeXp02bNqlPnz7VeWsAAIB6p9I7XJMnT9a2bdt06tQp9e3bV0888YQefvhhPfnkk1qxYoVatmypV199VZLUr18/bdiwQQMHDlRISIjmzJkjSYqIiNCECRM0atQoSdLjjz+uiIgICz8WAABA3VFp4Hr55Zcr7F+2bNl5fTabTc8//3yFx48aNcoMXAAAAFcSVpoHAACw2GX/ShEAADQcecUOFZS5zHZjj+HDahoOAhcAADAVlLmUvv8Xs53QisBVEwhcAABcyYpPSY4CsxnqCfZhMQ0XgQsAgCuZo0A6uN5s+rfqKynQd/U0UEyaBwAAsBiBCwAAwGIELgAAAIsxhwsAgCtYqdOjkmKH2TZYBsISBC4AAK5gDpdb++2FZrvTdR51Dcs328E8DKsRBC4AAGCyuUtUtCfNbPvdPMCH1TQcxFYAAACLEbgAAAAsxiNFAACuEOX3SZTYK7G2ELgAALhClN8nUWKvxNrCI0UAAACLEbgAAAAsRuACAACwGIELAADAYkyaBwDgChHqKfBaRV5iJfnaQuACAOAK4e8oVNGeL7z6GvpK8gGBQco8VWy2mwQHKLxxUO3XUevvCAAAUEvK3IbXUhh9OzTzSeDiPiIAAIDFCFwAAAAWI3ABAABYjMAFAABgMQIXAACAxQhcAAAAFiNwAQAAWIx1uAAAaKiKT0mOArMZKIcPi7myEbgAAGigSovyVJJxdmX54LY3+7CaKxuBCwCABsrhcmu/vdBsd27rw2KucMzhAgAAsBiBCwAAwGI8UgQAoIHIK3aooMxltht7DB9Wg3MRuAAAaCAKylxK3/+L2U5oReCqKwhcAADUR+WWfJCkUE+wj4pBZQhcAADUR44C6eB6ry7/Vn0lBfqmHlzUZQWu+Ph4hYaGys/PT/7+/lq5cqVyc3P11FNP6aefflKrVq306quvKjw8XIZh6MUXX9SGDRvUqFEjzZs3T506daqpzwEAAFBnXfavFJctW6bVq1dr5cqVkqQlS5aoV69eWrdunXr16qUlS5ZIktLT03X48GGtW7dOs2bN0gsvvHC5bw0AwBWt2OHWqWKH+cfFJPk6q8aXhVi/fr1GjBghSRoxYoTS0tK8+m02m+Li4pSfn6/s7OyafnsAAK4YZf9Z2PTMnyCbR13D8s0/wXL6ukT8x2XP4Ro3bpxsNpvuvvtu3X333crJyVFMTIwkqXnz5srJyZEk2e12tWjRwjyvRYsWstvt5rEAAODy2NwlKtqTZrb9bh7gw2pwrssKXB988IFiY2OVk5OjsWPHql27dl6v22w22Wy2al/f4/EoIyPjckpEJUpLSxljCzG+1mOMrcX4Wutyxrd5YKlKCouUl59n9nncniq1q3OOw+mU2+Gw/JyauIZRVKiAoCgdzzpu9uVE2VSQdUS17bICV2xsrCQpOjpaAwcO1K5duxQdHa3s7GzFxMQoOztbUVFR5rFZWVnmuVlZWeb5F+Ln56eOHTteTomoREZGBmNsIcbXeoyxtRhfa13W+OYe1amfQxXe9Oy8LT9/P4U3Db/kdnXOCQoMlDsoyPJzauIaoaFhcvj56aoWV5l90c2i1TqyjaxwsfBc7TlcxcXFKiwsNP/+zTffqH379oqPj1dKSookKSUlRf3795cks98wDO3cuVNNmjThcSIAALgiVPsOV05Ojh5//HFJktvt1tChQ9W3b1916dJFTz75pFasWKGWLVvq1VdflST169dPGzZs0MCBAxUSEqI5c+bUzCcAAOAKUH7bnqalTjndHh9WVPdFhdhks51SUJjb7Du9OGzjWq+l2oGrTZs2+uSTT87rj4yM1LJly87rt9lsev7556v7dgAAXNHKb9vTNaxE0eStiwpwl6rk4CYVFZ4Nqv5RIyRdfEqTFWp8WQgAAAB4I3ABAABYjL0UAQCoB0I9Beoalm+2o4J5nlifELgAAKgH/B2FKtrzhdmOibtN7oscj7qFwAUAQG0rPiU5CtQ8sFTKPSoFNZEaR1Z4zBmBctRykahJBC4AAGqbo0A6uF7OrCypRQvpuv7nB67/HHOGrcUNtVwkahKT5gEAACxG4AIAALAYjxQBAKiDSp0elRSfnbcVbFzkYNR5BC4AAOogh8ut/fZCs925rQ+LwWXjkSIAAIDFCFwAAAAW45EiAAB1QF6xQwVlZzdZbuxh0lZDQuACAKCWnZkQ7/IL1qlih/xLnPrFUaatP54yj0loReBqSAhcAADUsjMT4vPy8xTe1FBodIn8owhYDRmBCwCAmlZuW54Kt+6BT3gMKfNUsdluEhyg8MZBlr8vgQsAgJpWblueCrfugU+4PB6l7//FbPft0KxWAhe/UgQAALAYd7gAAKhh5VeJDyhzKu+cx1jlf4EYFWJTkNuurmFlZl8w90QaFAIXAAA1rPwq8de28yj98NnHWOV/gRjgLpVzX7qKTp4NZX43D7C+UNQaAhcAAFVQfr0sqfYmXqP+InABAFAFBWUur0nXUu1NvEb9xQNiAAAAi3GHCwCAy9C+qUthJceVX3q2z9/juPAJuCIRuAAAuJhyi5iGeoK9Xg7xFKskY6OOnjPhvTMT3lEOgQsAgIspt4ipf6u+kgJ9Vw/qJQIXAAAXUX5NLcPDnoeoOgIXAAAXUX5NrU7XedQ1LN9sRwV7Kr1GIz/D6xwWNb3yELgAAKgCm7tERXvSzHZM3G1yV/EcFjX1nfLh9/ScvMaWvy+BCwCAM8pNkJekQPGLw4akfPj1jxohKdby9yVwAQBwRrkJ8pJka3GDj4pBQ8JDZAAAAItxhwsAUC+U38OwJvYvLH/NpqVOGSUOnftDxGB+lIgaQOACANQL5fcwHHxdsOQoO3tAUBOpceRlXbNrWImiHR7vRUzbVr9m4AwCFwCgXgj1FHgvrVDmr1MH0822f/sBMhTqdderOO8XuYrzzHZA43A1Dm9WOwUD5yBwAQDqngp+LdjIUaSiPV+YbePmAV7rY7VpXarGxjHln81X8vc4lLF5rdnucutw5Z8TwML9A9Q17Oz7XMqaWkB1ELgAAHWPo0DFe9apzHV2havgtjdf9JQAd6lKMtIvuqeh4SxWxrdpXq9XdU0tNCweQ8o8dfY7UxNzAytC4AIA+F75O1rOUpWVW+GduVSwgsvj8ZrH17dDMwIXAKDuKf9Lv1BPgUI8JWoU+J+VhyqYzF7+nHDHSbn2nb3TFNLuZjndPN5Dw1HrgSs9PV0vvviiPB6PRo8erYcffri2SwAA1CBPySnlZp0w237BHrkzN6vEzyZJatQhXraivLMBTJLHHaz0g2d/YZjQyqND59zNanuVS+QtNCS1GrjcbreSk5O1dOlSxcbGatSoUYqPj9d1111Xm2UAAP6jJta28ncUek1mj4m7TU63oUMniiRJba/Kl99P35kBTJIC28era9jZwMVmzvCV2tpbsVYD165du3TNNdeoTZs2kqTExEStX7+ewAUANaH8PKjyj/Iq+OVfgFPKzT77j01ETBPl550NYC2aBqjAfkjGOXebGgUHK8jvbMel7DV4bgCTpM5ti9nMGXVC+b0VbZEjlHmqidcxNTGRvlYDl91uV4sWLcx2bGysdu3aVZslAECDVVqUp5KMs3eaAv5rgPLKgs1209KTMg6mea+i3vZm739smg7w+hVfq0695X98h9cv/7rdPECnfvzW6xpAQxEgt3J//rdXX2iL5lLjy9vg2mYYRq1tWvD5559r48aNevHFFyVJKSkp2rVrl2bOnFnh8Tt37lRwcHCFrwEAANQlZWVliouLq/C1Wr3DFRsbq6ysLLNtt9sVG3vhxHihogEAAOqTWp2l2KVLFx0+fFjHjh2Tw+FQamqq4uPja7MEAACAWlerd7gCAgI0c+ZMjR8/Xm63W3feeafat29fmyUAAADUulqdwwUAAHAlYuETAAAAixG4AAAALFZn91JkCyBrxcfHKzQ0VH5+fvL399fKlSt9XVK9N3XqVH399deKjo7WmjVrJEm5ubl66qmn9NNPP6lVq1Z69dVXFR4e7uNK66eKxnfhwoX66KOPFBUVJUmaPHmy+vXr58sy663jx4/r2WefVU5Ojmw2m+666y6NGTOG73ANutAY8z2uGWVlZbrvvvvkcDjkdrs1aNAgTZo0SceOHdPkyZOVm5urTp066aWXXlJQUM1vTl0pow5yuVxG//79jaNHjxplZWXGsGHDjAMHDvi6rAbl9ttvN3JycnxdRoOybds2Y/fu3UZiYqLZ99///d/G4sWLDcMwjMWLFxsvvfSSr8qr9yoa3wULFhhvvvmmD6tqOOx2u7F7927DMAyjoKDASEhIMA4cOMB3uAZdaIz5HtcMj8djFBYWGoZhGA6Hwxg1apSxY8cOY9KkScaaNWsMwzCMGTNmGO+//75P6quTjxTP3QIoKCjI3AIIqMt69Ohx3v/5r1+/XiNGjJAkjRgxQmlpaRWdiktQ0fii5sTExKhTp06SpLCwMLVr1052u53vcA260BijZthsNoWGhkqSXC6XXC6XbDabvv32Ww0aNEiS9Nvf/tZneaJOBq6KtgDiS1nzxo0bp5EjR2r58uW+LqXBysnJUUxMjCSpefPmysnJ8XFFDc/777+vYcOGaerUqcrLy/N1OQ1CZmamMjIy1K1bN77DFjl3jCW+xzXF7XYrKSlJt9xyi2655Ra1adNGTZs2VUDA6RlULVq08FmeqJOBC9b74IMPtGrVKv3lL3/R+++/r++++87XJTV4NptNNpvN12U0KPfee6/+/ve/a/Xq1YqJidG8efN8XVK9V1RUpEmTJmnatGkKCwvzeo3vcM0oP8Z8j2uOv7+/Vq9erQ0bNmjXrl3697//XflJtaROBq6qbgGEqjszntHR0Ro4cCCbiFskOjpa2dnZkqTs7GxzUixqRrNmzeTv7y8/Pz+NHj1a//znP31dUr3mdDo1adIkDRs2TAkJCZL4Dte0isaY73HNa9q0qXr27KmdO3cqPz9fLpdLkpSVleWzPFEnAxdbAFmruLhYhYWF5t+/+eYbVvy3SHx8vFJSUiSd3qy9f//+Pq6oYTkTBCQpLS2N7/FlMAxD06dPV7t27TR27Fizn+9wzbnQGPM9rhknT55Ufn6+JKm0tFSbN2/Wtddeq549e+qLL76QJK1atcpneaLOrjS/YcMGzZkzx9wC6LHHHvN1SQ3GsWPH9Pjjj0s6/bx76NChjG8NmDx5srZt26ZTp04pOjpaTzzxhAYMGKAnn3xSx48fV8uWLfXqq68qIiLC16XWSxWN77Zt27R3715JUqtWrZScnGzON0LVfP/997rvvvvUoUMH+fmd/n/xyZMnq2vXrnyHa8iFxnjNmjV8j2vA3r17NWXKFLndbhmGoTvuuEMTJ07UsWPH9NRTTykvL08dO3bU/PnzfbIsRJ0NXAAAAA1FnXykCAAA0JAQuAAAACxG4AIAALAYgQsAAMBiBC4AAACLBfi6AAANwxtvvKE1a9bIz89Pfn5+Sk5ONrctqa5ffvlF06dP1/Hjx+VyudSqVSv95S9/qdI1Dh06pMmTJ8tms2nBggW6+uqrL6umc+3bt0/PPvusJOn48eMKCwtTkyZNFBkZqbfffrvG3gdA/ceyEAAu244dOzRv3jy9++67CgoK0smTJ+V0Oi97ReeZM2fq2muv1ZgxYySdXmfn17/+9SWf73a79de//lUul0sTJky4rFoqM2XKFN1222264447LH0fAPUTd7gAXLYTJ04oMjLSXEzw3O1fdu/erXnz5qm4uFiRkZGaO3euYmJi9NFHH2n58uVyOp265ppr9NJLLykkJMTrutnZ2erdu7fZPhO2tm7dqrfeekuLFy+WJCUnJ6tz584aOXKk4uPjNXjwYG3evFm///3vtWzZMvn5+WnLli169913NWHCBGVlZamsrEwPPvig7r77bklSenq6XnnlFbndbkVGRmrZsmUqLi7WrFmzdODAAblcLk2cOFEDBgyodDw2bdqkhQsXyuFwqE2bNpo7d65CQ0MVHx+vxMREpaeny9/fX7NmzdLLL7+sI0eOaNy4cbr33nu1detWLViwQKGhoTpy5Ih69uypF154wVwoE0A9ZQDAZSosLDSGDx9uJCQkGM8//7yxdetWwzAMw+FwGHfffbeRk5NjGIZhpKamGlOmTDEMwzBOnjxpnv/yyy8b77zzznnXTU9PN2688Ubj/vvvN15//XUjKyvLMAzD+Pbbb42HH37YPO5Pf/qT8fHHHxuGYRi33367sWTJEvO1BQsWGG+++abZPnXqlGEYhlFSUmIkJiYaJ0+eNHJycoy+ffsaR48e9Trmf/7nf4yUlBTDMAwjLy/PSEhIMIqKiiocg+eee8747LPPjJycHON3v/udedzixYuNhQsXmrW9//77hmEYxosvvmgMHTrUKCgoMHJycoxevXqZn61z587G0aNHDZfLZTz00EPGZ599dtHxB1D3cYcLwGULDQ3VypUr9f3332vr1q166qmn9PTTT6tz587av3+/uW+cx+NR8+bNJUkHDhzQq6++qoKCAhUVFalPnz7nXffWW29VWlqaNm7cqPT0dP32t7/VmjVrKq1nyJAhF3zt3Xff1d///ndJp+ddHTlyRCdPnlT37t3Vpk0bSTK3rtm0aZO+/PJLvfXWW5KksrIyHT9+XNdee+0Fr/+Pf/xDBw8e1L333ivp9GbFcXFx5utn9iLs0KGDiouLFRYWJkkKCgoy94Hr2rWrWUtiYqJ++OEHHlUC9RyBC0CN8Pf3V8+ePdWzZ0916NBBKSkp6tSpk9q3b6/ly5efd/yUKVP0+uuv69e//rVWrlypbdu2VXjdiIgIDRs2TMOGDdMjjzyi7777TtHR0fJ4POYxZWVlXueUfzR5xtatW7V582YtX75cISEheuCBB847t7wFCxaoXbt2lX18k2EY6t27t15++eUKXw8MDJQk+fn5ee3n5ufnJ5fLJUmy2Wxe55RvA6h/mBQA4LL9+9//1uHDh812RkaGWrZsqbZt2+rkyZPasWOHpNN3ew4cOCBJKioqUvPmzeV0OvXpp59WeN0tW7aopKREklRYWKijR4/qqquuUqtWrXTo0CE5HA7l5+dry5Ytl1RnQUGBwsPDFRISokOHDmnnzp2SpLi4OH3//fc6duyYJCk3N1eS1KdPH7333nsy/vPboj179lT6HnFxcdq+fbuOHDkiSSouLtaPP/54SfWdsWvXLh07dkwej0efffaZbrzxxiqdD6Du4Q4XgMtWXFys2bNnKz8/X/7+/rrmmmuUnJysoKAgLViwQLNnz1ZBQYHcbrfGjBmj9u3b6w9/+INGjx6tqKgodevWTUVFRedd91//+pdmzZolf39/GYah0aNHq2vXrpKkO+64Q0OHDlXr1q31m9/85pLq7Nu3rz788EMNHjxYbdu2NR/1RUVFKTk5WU888YQ8Ho+io6O1dOlSTZgwQXPmzNHw4cPl8XjUunVrc6L+hURFRWnu3LmaPHmyHA6HJOnJJ59U27ZtL3k8u3TpolmzZpmT5gcOHHjJ5wKom1gWAgDqkPK/wATQMPBIEQAAwGLc4QIAALAYd7gAAAAsRuACAACwGIELAADAYgQuAAAAixG4AAAALEbgAgAAsNj/B1QvS7V+IdQSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor(max_depth=15, random_state=0)\n",
    "Predictive_Model(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "executionInfo": {
     "elapsed": 300,
     "status": "ok",
     "timestamp": 1669751582097,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "oHkm2GydxaLD",
    "outputId": "e4362cb5-f569-48c1-92f7-f7e7280cd842"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FeatureBar(dt.feature_importances_, 'Feature Importance', 'Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BPINC943yck2"
   },
   "source": [
    "The decision tree does not create any coefficients for the features used in the prediction. However, by calculating how much the mean squared error decreases by selecting a feature for splits, we can measure the importance of each feature in the decision tree model. \n",
    "\n",
    "In our dataset, air temp still appear to be most important in the predictions, and also noticed few other features like Longitude, Meridional Winds and Humidity to have a minute effect on sea surface temp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H4pAX6bUzA7_"
   },
   "source": [
    "Performance Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "executionInfo": {
     "elapsed": 1771,
     "status": "ok",
     "timestamp": 1669751586899,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "OvqcMXfJzDas",
    "outputId": "aa57e630-1ef6-4707-e03c-0e1bb2bf6312"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of Basic Results:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-0b4c4f5d-290c-4a66-b822-be7e21264e68\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>R squared</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.912069</td>\n",
       "      <td>0.552486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.935877</td>\n",
       "      <td>0.471798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.939816</td>\n",
       "      <td>0.457080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b4c4f5d-290c-4a66-b822-be7e21264e68')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-0b4c4f5d-290c-4a66-b822-be7e21264e68 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-0b4c4f5d-290c-4a66-b822-be7e21264e68');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                   R squared      RMSE\n",
       "Linear Regression   0.912069  0.552486\n",
       "KNN                 0.935877  0.471798\n",
       "Decision Tree       0.939816  0.457080"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = ['Linear Regression', 'KNN', 'Decision Tree']\n",
    "models = [LinearRegression(), KNeighborsRegressor(n_neighbors=5), DecisionTreeRegressor(max_depth=15, random_state=0)]\n",
    "R_squared = []\n",
    "RMSE = []\n",
    "for m in models:\n",
    "    m.fit(train_X, train_y)\n",
    "    prediction_m = m.predict(test_X)\n",
    "    r2 = metrics.r2_score(test_y, prediction_m)\n",
    "    rmse = np.sqrt(metrics.mean_squared_error(test_y, prediction_m))\n",
    "    R_squared.append(r2)\n",
    "    RMSE.append(rmse)\n",
    "basic_result = pd.DataFrame({'R squared':R_squared,'RMSE':RMSE}, index=regressor)\n",
    "print(\"Summary of Basic Results:\")\n",
    "basic_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EOHiiAeuzP0C"
   },
   "source": [
    "In the table above, the *Decision Tree* seems to be the optimal model to predict the Sea Surface Temp in Pacific Ocean. However, it is too early to make a conclusion since there are more things to be considered. \n",
    "\n",
    "The first is that we use only one specific selection of a training and testing sets, and the second is that for each model we chose one specific value for each hyper parameter. Since the values were chosen arbitrarily for the hyper parameters, the results can vary according to what values we choose for those parameters.\n",
    "\n",
    "To get a robust result covering these issues, we need to go through the cross validation and grid search process as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eErz1y-Izlid"
   },
   "source": [
    "### Cross Validation and Grid Search\n",
    "Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 164,
     "status": "ok",
     "timestamp": 1669751600074,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "LGNqRk22zoJv"
   },
   "outputs": [],
   "source": [
    "scoring={'R_squared':'r2', 'Square Root of MSE': 'neg_mean_squared_error'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 156,
     "status": "ok",
     "timestamp": 1669751601859,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "sG1oJMqBzs4M"
   },
   "outputs": [],
   "source": [
    "def CrossVal(estimator):\n",
    "    scores = cross_validate(estimator, X, y, cv=5, scoring=scoring)\n",
    "    r2 = scores['test_R_squared'].mean()\n",
    "    mse = abs(scores['test_Square Root of MSE'].mean())\n",
    "    print('R_squared:', r2)\n",
    "    print('Square Root of MSE:', np.sqrt(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 322,
     "status": "ok",
     "timestamp": 1669751603922,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "pflwHH-gzvLG",
    "outputId": "f9aa56ea-f890-4dc3-ac1e-bb0e2a3a9301"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared: 0.8991446822431355\n",
      "Square Root of MSE: 0.5682419569232426\n"
     ]
    }
   ],
   "source": [
    "CrossVal(LinearRegression())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ltMrC52z-nH"
   },
   "source": [
    "Since the linear regression does not have any hyper parameter in our analysis, only CV is performed here. The number of the folds in the CV is set to be 5. The average of the R² is 0.89 and the RMSE is 0.56"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KYoIdCAb0N4f"
   },
   "source": [
    "K-Nearest Neighbors:\n",
    "The hyper parameter for KNN we use in this analysis is the number of the nearest neighbors(n_neighbors). The range for the grid is the integers from 5 to 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "atqiDFij0hDg"
   },
   "source": [
    "Since GridSearchCV in sklearn includes the process of the Cross Validation, the process of performing cross_validate is omitted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 124,
     "status": "ok",
     "timestamp": 1669751608353,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "kDiVn__X0Xam"
   },
   "outputs": [],
   "source": [
    "# Create a grid search function that takes in the estimator, features and parameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def GridSearch(estimator, Features, Target, param_grid):\n",
    "    for key, value in scoring.items():\n",
    "        grid = GridSearchCV(estimator, param_grid, cv=10, scoring=value)\n",
    "        grid.fit(Features,Target)\n",
    "        print(key)\n",
    "        print('The Best Parameter:', grid.best_params_)\n",
    "        if grid.best_score_ > 0:\n",
    "            print('The Score:', grid.best_score_)\n",
    "        else:\n",
    "            print('The Score:', np.sqrt(abs(grid.best_score_)))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 176811,
     "status": "ok",
     "timestamp": 1669751792344,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "J4mUcWSt0RRH",
    "outputId": "73fe22c5-2409-4ca5-d7aa-b760d75f005c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared\n",
      "The Best Parameter: {'n_neighbors': 16}\n",
      "The Score: 0.8510733582554536\n",
      "\n",
      "Square Root of MSE\n",
      "The Best Parameter: {'n_neighbors': 16}\n",
      "The Score: 0.6009227105104002\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_grid = dict(n_neighbors=np.arange(5,26))\n",
    "\n",
    "GridSearch(KNeighborsRegressor(), X, y, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Ro5zMhixlJ2"
   },
   "source": [
    "The optimal number of n_neighbors is 16. The R² is 0.851 and the RMSE is 0.6. We can see how 16 is the optimal value for n_neighbors in our analysis by looking at the Validation Curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "executionInfo": {
     "elapsed": 173,
     "status": "ok",
     "timestamp": 1669752186267,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "lMwom_Pl1ego"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "def ValidationCurve(estimator, Features, Target, param_name, Name_of_HyperParameter, param_range):\n",
    "    \n",
    "    train_score, test_score = validation_curve(estimator, Features, Target, param_name=param_name, param_range= param_range,cv=10,scoring='r2')\n",
    "    Rsquared_train = train_score.mean(axis=1)\n",
    "    Rsquared_test= test_score.mean(axis=1)\n",
    "    \n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.plot(param_range, Rsquared_train, color='r', linestyle='-', marker='o', label='Training Set')\n",
    "    plt.plot(param_range, Rsquared_test, color='b', linestyle='-', marker='x', label='Testing Set')\n",
    "    plt.legend(labels=['Training Set', 'Testing Set'])\n",
    "    plt.xlabel(Name_of_HyperParameter)\n",
    "    plt.ylabel('R_squared')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "executionInfo": {
     "elapsed": 457617,
     "status": "ok",
     "timestamp": 1669752645762,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "fPvRYSUix3HS",
    "outputId": "1855f0b4-9e46-499b-dbe0-b6c1204a208b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN Validation Curve\n",
    "ValidationCurve(KNeighborsRegressor(), X, y, param_name='n_neighbors', Name_of_HyperParameter='K_Neighbors', param_range = np.arange(5,26), )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1mLAf4L2cgl"
   },
   "source": [
    "Decision Tree - hyper parameters selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 64521,
     "status": "ok",
     "timestamp": 1669752736290,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "Xa_TWLY22gts",
    "outputId": "af9fbe07-8716-4ee5-827b-f17b20d50041"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared\n",
      "The Best Parameter: {'max_depth': 9}\n",
      "The Score: 0.8887238489659964\n",
      "\n",
      "Square Root of MSE\n",
      "The Best Parameter: {'max_depth': 9}\n",
      "The Score: 0.5163982264777722\n",
      "\n"
     ]
    }
   ],
   "source": [
    "param_grid=dict(max_depth=np.arange(2,15))\n",
    "\n",
    "GridSearch(DecisionTreeRegressor(random_state=0), X, y, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ed6S7_j4D3Nz"
   },
   "source": [
    "The result indicates that the optimal value for the max_depth is 9. The R² is 0.88 and the RMSE is 0.51 under max_depth=9. \n",
    "\n",
    "Let's draw the validation curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "executionInfo": {
     "elapsed": 32733,
     "status": "ok",
     "timestamp": 1669753450354,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "oiOCKo-rEJGV",
    "outputId": "b17ecb85-04c8-41d6-ae42-82273480e6aa"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAE9CAYAAAC/RJ6QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZiN9f/H8eeZGTO2rHHGMgbZl0JEUZNRdiFLiyKRdloQKUpZUumnlKWFr1KWUtLUN8WUii+Skoxky4xl7NuYfc7vj4/ZV+Ocuc8583pc17nOOffZ3nNbzms+q83hcDgQEREREbfhY3UBIiIiIpKZApqIiIiIm1FAExEREXEzCmgiIiIibkYBTURERMTNKKCJiIiIuBk/qwtwpt9//52AgACXf058fHyRfE5xofPpfDqnzqXz6Xw6p86l8+l8RXFO4+PjadGiRY6PeVVACwgIoHHjxi7/nIiIiCL5nOJC59P5dE6dS+fT+XROnUvn0/mK4pxGRETk+pi6OEVERETcjAKaiIiIiJtRQBMRERFxM141Bi0niYmJREVFERcX59T3zKvfWKBkyZLUrFmTEiVKWF2KiIiIx/H6gBYVFcUVV1xB7dq1sdlsTnnP2NhYSpUq5ZT38kYOh4MTJ04QFRVFnTp1rC5HRETE43h9F2dcXByVK1d2WjiT/NlsNipXruzUVksREZHixOsDGqBwZgGdcxERkcIrFgHNSqdOnaJ379707t2b9u3bc+ONN6bdT0hIyPO1f/75Jy+//HK+n3HnnXc6pdbY2FiefvppevXqRc+ePbnrrruIiYnJ8zVz5851ymeLiIi4hcWLoXZtGjVtCrVrm/sW8PoxaFarWLEiK1euBOCtt96idOnSDBs2LO3xpKQk/Pxy/mNo3rw5zZs3z/czlixZ4pRaFy1axJVXXsnrr78OwN69e/Md5D9v3jweeughp3y+iIiIpRYvhhEj4MIFbAD//mvuAwwaVKSlKKBltXgxTJgABw5ArVowZYrT/1DGjRuHv78/ERERtGrVih49ejBlyhTi4+MpWbIkU6dOpW7dumzcuJEPPviAefPm8dZbb3Ho0CGioqI4dOgQQ4YMYfDgwQC0bNmSrVu3snHjRmbPnk3FihXZtWsXTZs25bXXXsNms/Hjjz8ybdo0SpcuTatWrYiMjGTevHmZ6jp27BjVq1dPu1+3bt202ytXruTDDz8kMTGRa665hkmTJvHGG28QFxdH7969qVevXlqwExER8TgXLsDYseY66/EJExTQLJUhOQMuTc7R0dEsWbIEX19fzp8/z+LFi/Hz82P9+vW88cYbvPXWW9les2/fPhYtWsT58+fp1q0bd911V7YWrh07dhAWFkbVqlW566672LJlC82bN2fixIl89NFHBAUF8dRTT+VYU79+/bj//vv59ttvadeuHX379qV27drs2bOHb775hk8++YQSJUrwwgsvsGrVKkaPHs3ixYvTWghFRETcTnIyREfDoUNw8KC55HT79Onc3+PAgaKr96LiFdAWLYIPPsj98f/9D+LjMx+7cAGGDYN330075J+SAj4Xh+/dfz9cbMm6FF27dsXX1xeAc+fO8cwzz/Dvv/9is9lITEzM8TUhISH4+/tTqVIlKlWqxIkTJwgMDMz0nKuvvjrtWKNGjTh48CBlypQhKCiIoKAgAHr06MGyZcuyvX/jxo35/vvv+eWXX1i/fj39+/dn6dKlbNiwge3bt9O/f38gfWasiIiIZRwOOHs2c9jKKXwdOWJCWka+vhAYCDVqQIMGcPPN5vbrr8OJE9k/q1atIvmRMipeAS0/WcNZfscvQ8Z11GbNmkXbtm15++23iYqKSuu6zMrf3z/ttq+vL0lJSfk+JznrX8p8lClThs6dO9O5c2d8fHz48ccfKVGiBH379uXpp5++pPcSERFJcylDiBIS4PDh/Fu9cprIVqGCCVs1akDTpum3q1dPv121qglpWdWqlbknDaB0aVNrESteAW3w4Lxbu2rXNt2aWQUHww8/pN1NcPJCtefOncNutwPw+eefO+19U9WpU4fIyEiioqKoWbMmX3/9dY7P27JlC/Xq1aN8+fIkJCSwe/durrvuOurVq8cjjzzCfffdR+XKlTl9+jQxMTHUqFEDPz8/EhMTtWOAiIjkLqchRMOGQXg4BAVlD19Hj2Z/D3//9JDVogV07549fFWvbgJVYaUGxgkTcBw4gM1FY9ELongFtPxMmWJJch4+fDjjxo1jzpw5hISEOP39S5YsyaRJkxg+fDilS5emWbNmOT4vMjKSF154AYCUlBRCQkLo0qULNpuNJ554gvvvv5+UlBRKlCjBxIkTqVGjBgMHDuS2226jSZMmmiQgIlKcxcVl725Mvaxcmb03Kj4e3n/f3K5SJT1stWmTc6tX5cpQFGtsDhoEgwaxMyKCxo0bu/7zcmFzOBwOyz7dySJyOJk5HctTAZpgPXGrp5iYGMqUKYPD4eDFF1+kdu3a3HfffS79zIKe+0v+M5J86Zw6l86n8+mcOpdLz2dKihmXlVPwytjqldPYrdKlTbj655+c39tmM8Euw/Acd1EUf0fz+gy1oGV1MTl7m+XLl/P555+TmJhI48aNueOOO6wuSURELtfFRoVGhV0aKjY2+/iurJfDh82YsIxsNrDbTfiqUwfat09v6cp4KV/ePDe3IUS1arllOHMHCmjFxH333efyFjMRESlCeS2qetddcOxYzgPsM15Oncr+vmXKpAesG2/M3tVYo4aZAXkpY48tGkLkyRTQREREPMn58yZwPfVUzouqDhkCQ4dC1iWbfHzSW72uugpuuinnsV7lyjl/rFeGwfeuXAjemyigiYiIuIOEBLNmV8YWr0OHMt8+eBDOncv7fZKTYcyYzC1e1aubVq9cthYsEl46hMhVFNBERERcKSUFjh/PHrSyhrCclpYoUcKEq+rVoVkz6Nw5PXA9/XTOrwkOhmnTXP9ziUspoImIiGRV0EVVM65kn1Nr16FDZpB91u5Gm80sllq9OtSsCdddl3ktr9TrypXTd67JymbTuC4vpoDmYqdOnUobnH/8+HF8fHyoVKkSYGZW+ucze2Xjxo2UKFGCVq1aAfDJJ59QqlQp+vTpc9m1hYeHM2vWLFJSUkhKSmLw4MHceeedBa5FRMQr5bSo6tCh8MknZpX6jGHs/Pnsry9XLj1g3Xxz5sCVevtSB9nnxI0WVRXnU0BzsYoVK6ZtJv7WW29RunRphg0bVuDXb9q0idKlS6eForvuusspdSUmJvL888/z6aefEhgYSEJCAlFRUZdUi4iIx0ntbsxt66BDh+DPP83zMkpMhLAws6RE9epwzTVmJfusrV7VqkHZskX387jJoqrifApoGcyYYRYw7tgx/Vh4OGzeDGPHOu9ztm/fzvTp07lw4QIVK1Zk2rRpVK1alUWLFrFkyRJ8fX2pV68eTz/9NEuWLMHHx4cvv/yS559/ng0bNqSFvHvvvZerr76ajRs3cu7cOaZMmULr1q2JjY1l3Lhx/PPPP9SpU4ejR48yceJEmjdvnlZDTEwMycnJVKhQATB7eNatWxeAkydPMmnSJA4dOgTAs88+i91uz1ZL69atnXdSREQu17lzuY/zKkh3Y40apjvzjz9yfn+bDfbudf3PIYICWiZt2sDAgbBsmQlp4eHp953F4XDw8ssv884771CpUiW+/vpr3njjDaZNm8b8+fNZu3Yt/v7+nD17lnLlynHnnXdmanXbsGFDpvdLTk7m008/5ccff2T27NksXLiQjz/+mPLly/P111+za9euHLtDK1SoQGhoKB07duT666/n5ptvpmfPnvj4+DBlyhSGDBlC69atOXToEMOGDeObb77JVouIyCUrzMKqiYmZN87O7Tqn2Y3lyqW3bmXtbsw4uzFjd2Nei6qKFJFiFdAWLYIPPsj7OdWrQ5cuppX68GFo3BhefNFcUqWk+KeN2bz//rz3X88qISGBXbt2MXTo0IvvlUKVKlUAaNiwIaNHj6ZTp07ccsstBXq/W2+9FYCmTZty8OBBwGx6PvhiUQ0aNKBhw4Y5vnbKlCn8/fffbNiwgQ8++ID169czffp01q9fz+7du9Oed/78eWJiYgr+Q4qI5CSnhVUfeMAMxG/RIvfwdfQoZN2VMLfZjVnDV2G6G7WoqriBYhXQCqJiRRPOUn+5q1jRue/vcDioX78+S5cuzfbY/Pnz2bx5M+Hh4cydO5dVq1bl+36pkwx8fHxITk6+5HoaNmxIw4YNue222+jUqRPTp08nJSWFZcuWERAQcMnvJyICmDW9Dh+GqCgTtKKi4IUXsi+sGhsLzz6b+VjqxtnVq0Pr1tlbvVI3zs5tduPl0qKq4gaKVUAbPDj/1q7Ubs3nn4c5c2DSpMxj0gBiYxMKvVm6v78/J0+eZOvWrbRs2ZLExET279/PVVddxeHDh2nXrh3XXnstYWFhXLhwgTJlynA+p1lCeWjVqhXffPMN7dq1Y/fu3ezatSvbc2JiYti+fTtt27YFYOfOndSoUQOADh068OGHHzJ8+HAgfTPXwtQiIl7o3Ln00JXT9cGDEB1d8Pez2eDnn9NnN7rDL4daVFUs5vKAtm7dOqZMmUJKSgoDBgxgROo+YRcdPHiQZ599lpMnT1KhQgVeffVVAgMDAWjcuDENGjQAoFq1asydO9eltWYcc9axo7lkvO8MPj4+vPnmm7z88sucO3eO5ORkhgwZQu3atRkzZgznz5/H4XAwePBgypUrR8eOHRk5ciRr1qzh+eefL9Bn3H333YwbN47u3btTt25d6tWrxxVXXJHpOQ6Hg/fee4+JEydSsmRJSpUqxbSLCxtOmDCByZMn06tXL5KTk2ndujWTJ0/OVosmCYi4kYKu25WXjDMc8wpfZ89mf22lSmY9rxo14NprzXXq/dTbLVqY+rKqVQtuuKFwP7eIl7I5HFk79p0nOTmZLl26sGDBAux2O/3792fmzJnUq1cv7TkjR46kY8eO9O3blw0bNrBixQpeffVVAFq2bMnWrVsL/HkROUwzzulYbgo6izM2NrbQLWhFITk5maSkJAICAjhw4AD33Xcf//3vf/Ndc83ZCnruL+XPSApG59S53P58Zl23C8yYqfnz00NaapdjfuEr6wxHX18z7iOnwJV6Xb06FOT/xILUKYXi9n9HPVBRnNO8PsOlLWjbtm0jODiYoKAgAHr06MGaNWsyBbQ9e/Ywfvx4ANq1a8ejjz7qypLylNNSGqktaZ4kNjaWwYMHk5SUhMPhYNKkSUUezkSkCI0fn/Om2Q88ADNn5t7lWKpUetDq0CHnEGa3m5DmDFpYVaTAXBrQoqOj07orAex2O9u2bcv0nEaNGrF69WqGDBnCd999R0xMDKdOnaJixYrEx8dz++234+fnx4gRI/Kd2RgfH09ERESmY4mJicTGxjrvh8J0Dzr7PZ3J19eXxYsXZzpmRb2JiYnZ/jxyEhcXV6DnScHpnDqXpeczJQXfEycocfgwJY4cocThw/gdPmzuX7z4nTiR40sdsbHElC1L4k03kVS1KomBgSRVrUpSYCCJVauSUq6cGf+VmzNnzMWZWrWCb74hLi6OkiVLmmP6u3rZ9G/e+aw+p5ZPEhg7diwvvfQSn3/+Oa1bt8Zut+N78be18PBw7HY7kZGRDBkyhAYNGlArj3VoAgICcuzidHZ3pLt3cbqLEiVKqIvTIjqnzuXS8xkTA5GRZmxWTpfISNM9mVHZsmbcVurYraVLcwxStuBgyv74o2vqvkz6O+pcOp/OV1RdnLlxaUCz2+0cOXIk7X50dDR2uz3bc2bPng2YmYWrV6+mXLlyaY8BBAUFcd1117Fjx448A1puHA4Htrx+SxSnc+HQRpGiU5hFVTNKSYEjR3IPXwcOQNbWLx+f9BXt27SBfv3Sw1jqpUKFzC1fN92kdbtEvIxLA1rz5s3Zv38/kZGR2O12wsLCeP311zM9J3X2po+PD/Pnz6dfv34AnDlzhlKlSqUtS/Hbb7+lLftwKUqWLMmJEyeoXLmyQloRcTgcnDhxIr37QsQT5bSoauos9NSQdu5c3q1fUVHZB92XL58etNq1yx6+qlcHv0v8r1nrdol4HZcGND8/PyZOnMjw4cNJTk6mX79+1K9fn1mzZtGsWTM6derEpk2bmDlzJjabjdatWzNp0iTATB6YNGkSNpsNh8PBAw88kGlyQUHVrFmTqKgojh075rSfKzExkRIZtwWRbEqWLEnNmjWtLkOk8J59NveB9zNmmGB26lTmx319zcD61K7HrOErKMgENFfQul0iXsXlY9BCQkIICQnJdGzUqFFpt7t27UrXrl2zva5Vq1YFWkk/PyVKlKBOnTqX/T4Zqa9fxEvEx5uWsb17zWXPnvTbOa3XBWbl++BguPHG7AGsWjXnzXgUkWLN8kkCIiIu43CYMV4Zg1fGMBYVlXmPx5IloW5dc9m3L+fNt4OD4csvi+5nEJFiSQFNRDxbQkLurWB792YPWdWqmQB2883m+qqr0kNZYGD64PvcFlXVwHsRKQIKaCJS9C5lW6LUVrCsLWAZW8FSUtKfX7Ik1KljgldISHr4qlvXHC9dumA1alFVEbGQApqIFK2sLVOpsyOjo6FZs5y7I7Pu/RgYaALXTTfl3Arm4+OcWi8OvN+pcaciUsQU0ESkaCQmmnFdTz6Z8+zIp59Ovx8QkB64brwxeytYmTJFW7uISBFTQBMR53E4TEvY33/Drl2Zr/fuhaSkvF+/bp0JYdWqOa8VTETEAymgicili4kxwStrCNu1K3N3ZEAA1K8PzZtD//7QoAGMG2dW188qdekKERFRQBORXCQnw/79OYewqKjMz61VCxo2hMGDTQhr2NBc16qVvSXMz0+zI0VE8qGAJuJNLnXvSIcDjh/PHsL+/tsM1s+4SXeFCiZ4hYZmDmH160OpUgWvUdsSiYjkSwFNxFvktXfk7bfD7t3p4StjIMu4XVGJElCvnglfvXqlh7CGDeHKKzNv0H05tC2RiEieFNBEvMWECTnPjhwyBO69N/OK+TVqmNB1xx2ZQ1hw8KVv1C0iIk6n/4lFPFViIvz5J2zcCJs2mRaznCQnw4svZu6SLFu2aGsVEZFLooAm4gkcDhPANm5Mv/z2G8TFmcerVDHjwGJjs782OBgmTizaekVE5LIooIm4o9OnTavYpk3pLWRHj5rHSpaEVq3g4YehbVu47jqoXRs+/lizI0VEvIQCmojVEhJg27b0lrFNm8wA/lSNG0O3biaMtW1r1hQrUSL7+2jvSBERr6GAJlKUHA6zon5qENu4EbZuhfh487jdbkLY4MGmZaxNGyhfvuDvr70jRUS8ggKaiCudPJm5m3LTJrPuGJgxY61bw2OPpbeOBQU5bykLERHxWApoIgVxcQHYPBdWjY+HP/7IPJB/927zmM0GTZrAbbeljxtr1kxLWoiISI707SCSnwwLwALpC8AeOWK6JFNbyH7/PX3l/WrVTBAbNsxcX3stlCtn3c8gIiIeRQFNJD+5LQA7erS5XaaM6ap84gnTMta2LdSsWfR1ioiI11BAE8lNYiJ8913uC8CCmX3ZpAn4+hZdXSIi4vUU0EQySkqCH36ApUthxQozyN9my7xNUqrgYLPkhYiIiJMpoImkpMDPP5tQ9umnZkHYsmWhd2+zV+XJk/DII1oAVkREiowCmhRPDocZ3L9kCSxfDgcPmmUvevY0oax7d3M/lZ9f/rM4RUREnEQBTYoPh8PMtFyyBJYtg/37wd/frNL/6qvQq1fum4hfXABWRESkKCigiff76y8TypYuhX/+Ma1ht94KL7xgujErVLC6QhERkUwU0MQ77dplAtnSpSag+fjAzTfDmDFw++1QubLVFYqIiORKAU28x/796aFs61ZzrEMHmD0b+vWDwEBLyxMRESkoBTTxbAcPmkH+S5aY1fzBLBY7cyYMGKAFY0VExCMpoInnOXrULIexZIlZHsPhgBYtYNo0GDgQ6ta1ukIREZHLooAmnuHkSbNw7JIlEB5u1i5r3NgM9L/jDmjY0OoKRUREnEYBTdzXmTOwcqUZU7Z6tVnlv149GD/ehLJmzcwq/yIiIl5GAU2ss3gxTJhAo4yLv/bpA6tWmVD2zTcQH28ee/JJuPNOaNlSoUxERLyeAppYY/FiGDECLlzABmZD8iFDzHIYiYlQrRo89JAJZW3bKpSJiEixooAm1pgwIfPelgDJyWZ7pe++M8tj+PpaU5uIiIjFFNDEGgcO5Hw8JgZCQoq2FhERETfjY3UBUgwdOGC2W8pJrVpFW4uIiIgbUkCTovXTT9CmjRlrFhCQ+bHSpc1EARERkWLO5QFt3bp1dOnShVtvvZX58+dne/zgwYMMGTKEXr16ce+993LkyJG0xz7//HM6d+5M586d+fzzz11dqrja3LkQGmo2J//9d3j/fQgOxmGzQXAwzJ8PgwZZXaWIiIjlXBrQkpOTmTx5Mu+99x5hYWF89dVX7N69O9NzXnnlFfr06cOqVat45JFHeP311wE4ffo0s2fPZtmyZSxfvpzZs2dz5swZV5YrrpKQAA8+CA8/DJ07my2ZGjUyYWz/fnb+9ZfZR1PhTEREBHBxQNu2bRvBwcEEBQXh7+9Pjx49WLNmTabn7Nmzh3bt2gHQrl27tMd//vln2rdvT4UKFShfvjzt27fnp59+cmW54gpHjphWs/nzzQKzX35pWtBEREQkVy4NaNHR0QQGBqbdt9vtREdHZ3pOo0aNWL16NQDfffcdMTExnDp1qkCvFTf366/QujVs3WoWnp06VUtniIiIFIDly2yMHTuWl156ic8//5zWrVtjt9vxLeSXeHx8PBEREU6uMLu4uLgi+RxPVu7LL6k2cSJJV15J1IcfEt+4MeRyznQ+nU/n1Ll0Pp1P59S5dD6dz+pz6tKAZrfbMw36j46Oxm63Z3vO7NmzAYiJiWH16tWUK1cOu93Opk2bMr32uuuuy/PzAgICaNy4sRN/gpxFREQUyed4pKQkGDsW3ngDbr4Z/2XLqFulSp4v0fl0Pp1T59L5dD6dU+fS+XS+ojineQVAl3ZxNm/enP379xMZGUlCQgJhYWGEhoZmes7JkydJSUkBYP78+fTr1w+ADh068PPPP3PmzBnOnDnDzz//TIcOHVxZrlyuEyegWzcTzh5/3Gxwnk84ExERkexc2oLm5+fHxIkTGT58OMnJyfTr14/69esza9YsmjVrRqdOndi0aRMzZ87EZrPRunVrJk2aBECFChV45JFH6N+/PwCPPvooFTS43H39+Sf07g0HD8IHH8DQoVZXJCIi4rFcPgYtJCSEkCxb94waNSrtdteuXenatWuOr+3fv39aQBM39tlnZqPzcuXgxx/h4qxcERERKRztJCCFl5ICzz8P/ftDs2Zm1qbCmYiIyGWzfBaneKizZ+Gee2DVKtOd+c47ULKk1VWJiIh4BQU0uXS7dpnxZv/8A2+9BY8+Cjab1VWJiIh4DXVxyqX55hu47jo4dgy++w4ee0zhTMRiM2ZAeHjmY+Hh5riIeCYFNCkYhwOmT4cePaB2bTPerGNHq6sSEaBNGxg4MD2khYeb+23aWFuXiBSeujglfxcuwP33m+2a7rgD3n8fypSxuiqRYisuDvbtg9270y+1a8Ott0K1aqaBe8AA8xyAOnWgZk3w0//4Ih5D/1wlb/v3Q9++8McfMG0aPPOMujRFikBMDOzdmzmEpV4iI02jdqry5aF+fXPZudPc//hj+Oij9Of4+kKtWias1aljAl3q7Tp1IDBQ/7RF3IkCmuTuhx/Mr+GJifDVV9C9u9UViXiVs2ezh689e8z1oUOZn3vllVCvHtx0k7nOeKlUyfxzHTjQrHwzZw78979w1VWmFS3jZf9+CAuDDLvwAWYSdsbQljXAVayoACdSlBTQJDuHA95+G554wvxKvnIlNGhgdVUiHunkyZxbwXbvNl2RGVWrZgJXly4mXKUGsKuugrw2Ukkdc7ZsmRka2rFj+v1OnXJ+TWysCWtZw9u+ffC//8GpU5mfX65c7uGtTp2CjXqYMcOMi8s4fDU8HDZvNlv4ugtPqVO8mwKaZBYfD488YrZr6tULPvzQ9JeIOImnfPkVtE6HwwSt3EJY1qATFGRCV58+mVvB6taFsmULV+vmzenhDMz1smXmeG5zeUqVgsaNzSUnZ87k3Pr2zz9mm90LFzI/v0qV3ANccDD4+6dPZli2zHSpZgyW7iRjnR07um+d4t0U0CTdoUPQr5/59fm55+DFF8FHE33FuTzlyy9jnVWrmusRI+CBB2D8+MzdkefOpb/Ox8cEknr14M47M4ewOnVMMHK2nIJtaktaYZUvDy1amEtWqaE0p9a3LVtgxQozMiKVzQY1apifv2VL6NkTWrQI4o8/4L77zFa+27eb57nL5emnTYju3x++/DJzABYpCgpoYmzcaCYDnD0Ly5eb/5VEnOzsWdNV9tBD5ku6aVPz5dy5s5kk/PHHZgex1EtysrX3ExJMF6GPTyOSk83P8NprZjZk3bomdN14Y3o3ZL16pvXI39/S0+xyNpsJrVWrQtu22R9PTja/7+UU4HbuNK1v69eb5sK33y7a2i/VBx+YCRbPPw833GAu118PdrvVlYm3U0ATWLDAfGPWqAHffgvNm1tdkXiw2FjTqrRrl+kO++ef9NvR0Zmfu3mzGZy+YYNpeUq9+Ppe3n1/f+e93+bNsHmzjW7d4MknTQgLCtKSFXnx9TXnKCjITGrIKLXF9LbbjrNy5ZW8/z506GBa5dzp8uuvpiPhllvMpIpTp2DWLHj1VfNzXHVVemC74Qbzy4avb9Gfa/Fe+i+mOEtMNO34b71lmgmWLoXKla2uSjxAQoJZAiJj+Eq9HRWV+bl2u5lj0qOHmXPSoIH5shs3Dh5+2Mw4XLrUPbuPwsNN19bDDx9j+fIq+PmZbjopnIzd2YGBx7jnniszdXe7i/BweOEF01WbsRv+yy/hiitg/XpzWb3aDNMFc7xdOxPW2rc3LYvlyln6Y4iHU0Arro4fT196/MknzYhoNQlIBklJ8O+/2UPYP/+Y7qqUlPTnVqpkglfHjukhrH5909qU9UsqPBwefDDnGR5d3yoAACAASURBVIfu9iWdHiaOM2BAFbes05NknMwQEVGwyQxWyGvSxdixJoSBaWnbty89sK1fDy+9ZP5t2GymMyJjK1vdulqqRApO38jF0e+/m9GvR47Af/4DgwdbXZE4QWFmR6akmBavrF2R//xjWsgyDvS+4goTuq67DgYNyhzEKlUqeJ2FmXFoBU8JE57EFZMZXKGgddpsJnTVrQv33GOOnT0LmzaZsPbLL2Zc5dy55rGqVTMHtmuvNV38IjlRQCtuli6FoUPNN+pPP2mzPi+S2xIGS5eaLJ7TmLDdu822QalKlTKtXk2bmjkjqavTN2hgvlyc8du/t31Ji2RUrpwZt3bLLeZ+cjLs2JG5le2LL8xjJUqYkJYxtFWrZl3t4l4U0IqL5GQzDWnaNPO/wGefmW9x8Qpnz5o/zmeegdtug4YNg9m+3QzS7t0bzp9Pf26JEmaAc/36ZkHU1BBWv76ZJ6KVVUScx9fXdHU2b2669gGOHjUTY1ID29tvw8yZ5rHatTMHtubNNfqkuNIfe3Fw+rTpk/r6a7OI01tvQUCA1VVJAaSkmPWmDh40XZEZrzPezrgOF8CWLaWpWNG0hnXrlt4VWb++2Y9R/+GLWKdqVfOLU+/e5n5CAmzdmh7YwsNN1yiYxYvbtk0PbO3ape8q4SmLPkvh6L9pb7R4MUyYAAcOmPbylBQzKeCdd8xyGhql6hYSEsxaUTmFr9TrQ4cyjwMD8xt5tWpQs6bpiuzc2dyuUcP8Zj55MgwYcIwVK6owdqy65ETcnb+/CWFt25o5Ww6H+e/7l1/SQ9uUKekTc5o2NWGtYkWztvinn5r/E9x10WcpHAU0b7N4sVnuPHUfltQdl597zqxpIJesML+lnj2be2tX6vXRo9lfV7p0eti68cb02xmvq1bNeb2l8HAYNSq19/o4d96pWYcinshmM7tRBAfD3XebY+fPp08+WL/erCd++rR57JZboGbNqzh2zKwx/tdfpuXdbjf/X9jt2uzeEymgeZsJE7JvkgdmsZ6XXir6erxAxsH3ISHw+ecwfLhZQm7OnIJ1OQJceaUJWTVqmPfMGLxSb5cvX/j/RDXrUMR7lS0LoaHmAqY1bedOE9Zmz4Y//vCndGnTNfrRR9lf7+eXHtZSrzPeznjsyivNWFWxlgKatzlw4NKOS74aNDDjuFJnZaV2Mzz/vLn29YXq1U3IatbMDLzPGryqV3f9dHrNOhQpPnx8oEkTszvHwYPpiymvXAlXX22OHz1qrjPeTr2OiDDX8fE5v3/lyvkHudTbpUvnX6/Gy106BTRvExSUcxirVavoa/FgDgesW2d+M/38cxPK6tQxa4N17WpmY6WGsNy6HEVEXOlyF1N2OMxwjKzhLWug++03c33mTM7vU7ZswVrnBgwwtYWGarxcQSigeZvQUFi4MPOx0qXNCFPJ1/nzpnvg7bdh+3YzbuPJJ6FFC3jiCdNqNmeO+Y2vdWurqxWR4uxyhzXYbGZYRfnyZoZ3fuLiTFDLK9Dt3WuWEDl+PPNuI6k6dTKtc+fOwe23m11JNmyARo3M/7eSLt+AtmDBgjwfHzp0qNOKkct06JDZPK5JE5M0IiNNy9mUKWaZDcnVrl1mkuuCBeY3yhYt4P334c47YePGzFsRuevWRCJSvBT1sIaSJc1XSkE6ZJKT4cSJ7K1xy5ebcXNVqpivqyVL0l9TtaoJao0aQcOG6beDg4tnL0W+AS0mJgaAffv28eeffxJ6cYRieHg4zZs3d211cmlGjjRrN6xcaRbAkjwlJ0NYmGktW73aDIodMAAefRSuvz59sL6nbE0kIuIufH1N4KpaNf1YeLhpL0jtiQgLMwvz7txpLn//ba5XrDAtcKkCAsxY4KzBrWFD073qrfINaI899hgAgwYNYsWKFZS9eDYee+wxHkxdFlmst2qVWV9hyhSFs3ycOGFax955x2wGXqOGWTvsgQdy3lxBg+9FRC5PxjFnWXsievY0l4yOH08PbKnXv/9uvuYydp3WrJlzq1uNGp6/rEiBx6AdP34cf3//tPv+/v4czxhxxTrnz5tmn6ZNYfRoq6txW1u2mEH/S5aYsRQ33wyvvWZW89aUchER17nUnogrrzSX9u0zH4+Phz17Mge3nTth0SIzPCVV2bLpgS1jcKtfP+8Z9e4027TAAa1Pnz7079+fW2+9FYDvv/+evn37uqwwuQTPP2/Gm/3yi1mSWtLEx5sxD7Nnm7FkZcrAffeZPNusmdXViYgUD87qiQgIMMOsmzTJfNzhgCNHsge3n38267enstnMjPyswa1RIzMuLuO6l4GB1s42LXBAe/jhh7npppv49ddfAZg2bRpNsp4hKXpbtsCbb5otnG64wepq3EZkJMydC+++a1bUbtAAZs2CIUPMjCUREfEeNpvZ7qpateyh78IFMxEsY3DbudOEr9jY9OdVrGiC2rXXQq9e0KePnW+/tW5C2CUtsxEbG0vZsmXp168fJ0+eJDIykqCgIFfVJvlJSjIDp6pWhWnTrK7Gcg6H+Qf39tvwxRfmWM+e8NhjZmq3j4+19YmISNErXdrMzG/RIvPxlBTzy3zW4LZtG8TEwOLFlXj+eevGGxc4oM2ePZvt27ezb98++vXrR2JiImPGjGFJxjmyUrTefBO2bjXxvkIFq6uxzLlzZvzB22+btYAqV4YxY0yjYu3aVlcnIiLuyMcnfc/Tzp3Tj4eHmxn9AwYcY86cKpZNCitwm8J3333HnDlzKFWqFAB2uz1tCQ6xwP79ZuxZz55md9xiKCICHn/czNZ57DEzvmzhQrMf5vTpCmciInJpUsecLV8OI0ceZ9kycz88vOhrKXBAK1GiBDabDdvFeasXctqQW4qGw2FGudtsptnI0+cSX4KkJLP10i23mEGi8+dDnz5mAsDmzWaMmav3vBQREe+U12zTolbgLs5u3boxceJEzp49y7Jly/jss88YOHCgK2uT3CxfDl9/DTNnFps9No8dg/feM4sbRkaaLUenToXhw83MGxERkcvlTuteFiigORwOunfvzt69eylTpgz79u1j5MiRtM+6QIm43unTMGoUtGpl+ve83KZNZomMpUvNJgmdOpmhdz17gp92khURES9VoK84m83GiBEjWLVqlUKZ1caNMxuahYV5bUKJizOBbPZs+PVXuOIKGDECHnkEGje2ujoRERHXK/AYtCZNmrBt2zZX1iL5+eUXmDcvvQXNg82YkX3Q5SefmGbkmjXNYrIxMWaI3cGD8NZbCmciIlJ8FLgJ5o8//mDVqlVUr149bSYnwKpVq/J83bp165gyZQopKSkMGDCAESNGZHr80KFDPPPMM5w7d47k5GRGjx5NSEgIUVFRdO/enTp16gBwzTXXMHny5Ev52bxLQoJpRqpVy2wc6eFSV2teuhT27SvD8OGwfr2Z9tynj5mVefPNxWr+g4iISJoCB7T333//kt88OTmZyZMns2DBAux2O/379yc0NJR6GTbznjNnDt26dePuu+9m9+7djBgxgrVr1wJQq1YtVq5cecmf65VefRV27DCbol/csN6TdexoWsy6d4f4+FrYbDBokFlvV2sfi4hIcVfgLs4aNWpQo0YNSpYsmbbchi2f5o1t27YRHBxMUFAQ/v7+9OjRgzVr1mR6js1m4/z58wCcO3eOqlWrFuLH8HL//AMvvWTWO+vZ0+pqnCIlxeyPFh9v7o8bBx99pHAmIiICl9CCtmbNGl555RWOHj1KpUqVOHToEFdddRVhYWG5viY6OprAwMC0+3a7Pds4tscee4xhw4bx0UcfERsby4IFC9Iei4qKok+fPpQtW5YnnniC1q1bX8rP5h0cDrMkfsmSZvqiF0hJgQcfNIvKli4NQ4Yc4913q3DrrdZtqSEiIuJOChzQZs2axdKlSxk6dChffPEF//vf//jyyy8vu4CwsDD69u3L/fffz9atWxk7dixfffUVVatWJTw8nIoVK7J9+3YeffRRwsLCKJtH9158fDwRERGXXVN+4uLiiuRzAMp/8QXV167l8MSJnD592iyz4cEcDnjppUCWLKlIyZIpzJ4dSYsWJ7nuugv061eDmTMP0ratFkG+XEX5d7Q40Pl0Pp1T59L5dD6rz2mBA5qfnx8VK1YkJSWFlJQU2rVrx9SpU/N8jd1u58iRI2n3o6OjsdvtmZ7z6aef8t577wHQsmVL4uPjOXXqFJUrV8bf3x+AZs2aUatWLfbt20fz5s1z/byAgAAaF8FUv4iIiCL5HI4fh9dfhxtuoNqkSVTz8N2+HQ4zAXXJEggJgYkTfQgNDSYi4gL33RdMcDBs3hys2ZpOUGR/R4sJnU/n0zl1Lp1P5yuKc5pXACzwN365cuWIiYmhTZs2jB49mpdffpnSpUvn+ZrmzZuzf/9+IiMjSUhIICwsjNDQ0EzPqVatGhs2bABgz549xMfHU6lSJU6ePElycjIAkZGR7N+/n6DiNkDp6afhzBmztIYXhLPRo81yGU8+aZbYyPJXgY4dc17FWUREpLgpcAvaO++8Q0BAAOPHj2fVqlWcO3eORx99NO839/Nj4sSJDB8+nOTkZPr160f9+vWZNWsWzZo1o1OnTowbN47nnnuOhQsXYrPZmD59Ojabjc2bN/Pmm2/i5+eHj48PL774IhUqVLjsH9hjrFkDixbBs89Cs2ZWV3NZHA4YP97sTPX446ZRUMtniIiI5K7AAS1ja1nfvn0L/AEhISGEhIRkOjZq1Ki02/Xq1WPJkiXZXtelSxe6dOlS4M/xKrGxZmJAvXrw3HNWV3PZJk6EV16Bhx+GWbMUzkRERPJT4IDWsmXLtGU1EhMTSUpKolSpUvz2228uK67YmjIFdu+G77+HDIsCe6LJk+Hll82m5rNnK5yJiIgURIED2tatW9NuOxwO1qxZw++//+6Sooq1v/4yzU333mt2BvdgU6fCpElm2yYvGEYnIiJSZAr1lWmz2bjlllv4+eefnV1P8ZaSYrZzKl/eDNTyYK++ChMmwD33wHvvKZyJiIhcigK3oK1evTrtdkpKCtu3bycgIMAlRRVb775rNqRcsACqVLG6mkJ74w0zG/POO82P4utrdUUiIiKepcABLTw8PO22r68vNWrU4J133nFJUcXS4cPwzDNmrYkhQ6yuptBmz4annoJ+/eDDD8GvwH/DREREJFWBvz6nTZvmyjpk1CiIizODtTx0JP28eWYZjT59zEboCmciIiKFU+Cv0JdffjnPx5/zguUgLBMWBsuXmw3R69e3uppCef99szJIz56wdCmUKGF1RSIiIp6rwEO34+Pj+euvvwgODiY4OJiIiAgSEhJo2rQpTZs2dWWN3u38eXjkEWjSxGOX0f/Pf+CBB6BrV/j0U7i4Q5eIiIgUUoFb0P7++28+/vhj/C72W915550MGjSIyZMnu6y4YmHSJDhwAH76ySOTzeLFMHSoWRFkxQrQvBEREZHLV+AWtDNnznD+/Pm0+xcuXODMmTMuKarY+O03+L//M0trdOhgdTWXbOlSGDwYbr4ZVq70+DV1RURE3EaBW9BGjBhB3759adu2LQ6Hg82bN/P444+7sjbvlpRkglmVKjB9utXVXLLPPoNBg6B9e1i1CjLsBCYiIiKXqcABrV+/ftx000388ccfAIwePZoqHrxWl+Vmz4YtW2DJEqhY0epqLsnKlWaNs7ZtzfyGMmWsrkhERMS7FLiLc8uWLZQpU4ZbbrmFmJgY3nvvPQ4ePOjK2rzXgQNmE/Ru3WDgQKuruSRhYTBgALRqBd98A1dcYXVFIiIi3qfAAe2FF16gVKlS7Ny5k4ULF1KrVi2eeeYZV9bmnRwOePRRc/3OOx615tm338Ltt8PVV5vb5cpZXZGIiIh3KnBA8/Pzw2az8f3333P33XczaNAgYmJiXFmbd/rsM/jqK5g8GWrXtrqaAvv+e7MAbZMmsHo1VKhgdUUiIiLeq8ABrUyZMsybN49Vq1Zx8803k5KSQlJSkitr8z5nzsDIkdCypdk5wEP88APcdptZQ/f776FSJasrEhER8W4FDmhvvPEG/v7+TJkyhSpVqnDkyBGGDRvmytq8z/jxEB0N8+d7zD5IP/0EPXpAnTomnFWubHVFIiIi3q/AAa1KlSoMHTqU1q1bA1C9enX69OmT9vgdd9zh/Oq8yYYNMHeu2azy4jl0d+vXQ/fuEBQEa9ZA1apWVyQiIlI8FDig5Sc+Pt5Zb+V9EhPNmmc1apj9Nj3Apk1m66Zq1WDtWggMtLoiERGR4sNp/Ww2D5qNWOReew22bzcLiHnAuhRbtkDnzmYN3bVroXp1qysSEREpXpzWgia52LPHzNi8/XYz0t7N/f473HqrmaW5di3UrGl1RSIiIsWP0wKaw+Fw1lt5D4cDHnoISpSAN9+0upp8/fkn3HILlC0L4eEQHGx1RSIiIsVToQNaSkoKX375Zdr9GTNmOKUgr7J4sZn6OH26GX/mxnbsgE6doGRJE87q1LG6IhERkeIr34B2/vx55s2bx+TJk/n5559xOBx8+OGH3HLLLXzzzTdpz2vQoIFLC/U4x4/Dk09Cu3amFc2N7dwJoaFm5Y+1a+Gqq6yuSEREpHjLd5LAmDFjKF++PC1atGD58uXMmzcPh8PB22+/TePGjYuiRs80ZgycPm3WPPNx36F+//xjwpnDYcKZcraIiIj18g1oUVFRzJkzB4ABAwbQoUMHfvjhBwICAlxenMcKD4eFC2HcOGje3OpqcrV3rwlniYlmt4BGjayuSERERKAAAc0vw4r3vr6+BAYGKpzlJS4OHnwQ6taFiROtriZX+/dDx45w4YJpOWva1OqKREREJFW+AW3nzp20atUKMDM14+PjadWqFQ6HA5vNxm+//ebyIj3K1Kmm33D1aihVyupqchQZaVrOzp41OwRcc43VFYmIiEhG+Qa0iIiIoqjDO+zYYWZsDhpkFhNzQwcPmpazEyfMBNOL2VtERETciGfs2O0JUlJM1+YVV8DMmVZXk6PDh004O3rUNPC1aWN1RSIiIpITBTRnef99+Plnc+2Gu4pHR5tuzUOH4NtvzeofIiIi4p4U0JzhyBEYOxZuvhmGDrW6mmyOHTOL0B44AN98A+3bW12RiIiI5EUBzRmeeMJMh5w7F9xs0/gTJ8z2TXv2QFgY3HST1RWJiIhIftx3BVVP8c03sHQpTJgADRtaXQ0zZphl2ABOnTJzFSIi4J57TBeniIiIuD8FtMsREwMPP2xWeH3mGaurAczA/4EDYdUq6NzZbIBeqhTcfbfVlYmIiEhBqYvzcrzwAvz7L6xbB26yeG/HjrBsGXTtCklJULYsfPGFOS4iIiKeQQGtsH7/Hd54A4YPhxtvtLqaTMqWhYQEc3vUKIUzERERT6MuzsJIToYHHoDKlc2gLzfz1FNmrsLYsTBnTvqYNBEREfEMCmiXYvFiqF2bRs2bw6+/Qv/+ULGi1VVlsmCBWY7tnnvglVdMd+fAgQppIiIinsTlAW3dunV06dKFW2+9lfnz52d7/NChQ9x777306dOHXr168eOPP6Y9Nm/ePG699Va6dOnCTz/95OpS87Z4MYwYAf/+S9pCGgsXmuNuZPZsMxzujTfM/dQxaZs3W1uXiIiIFJxLx6AlJyczefJkFixYgN1up3///oSGhlKvXr2058yZM4du3bpx9913s3v3bkaMGMHatWvZvXs3YWFhhIWFER0dzdChQ/n222/x9fV1Zcm5mzDBrHWW0YUL5vigQdbUlMW+ffDHHzBypOl9TdWxo8ahiYiIeBKXtqBt27aN4OBggoKC8Pf3p0ePHqxZsybTc2w2G+fPnwfg3LlzVL24TdKaNWvo0aMH/v7+BAUFERwczLZt21xZbt4OHLi04xZ49VXw8YGnn7a6EhEREbkcLm1Bi46OJjAwMO2+3W7PFrIee+wxhg0bxkcffURsbCwLFixIe+0111yT6bXR0dF5fl58fDwRERFO/AnSXRUYiP/hw9mOJwQGssdFn3kpjh3z4/33r6JPnzOcPXuEs2etrqjg4uLiXPbnVlzpnDqXzqfz6Zw6l86n81l9Ti1fZiMsLIy+ffty//33s3XrVsaOHctXX31VqPcKCAigcePGTq7woldfNWPQMnZzli6N/6uvuu4zL8GCBWbds6lTK1KvnntNXMhPRESEW5xDb6Jz6lw6n86nc+pcOp/OVxTnNK8A6NIuTrvdzpEjR9LuR0dHY7fbMz3n008/pVu3bgC0bNmS+Ph4Tp06VaDXFqlBg2D+fAgOxmGzQXCwue8G489OnjTLadxxB2QY3iciIiIeyqUBrXnz5uzfv5/IyEgSEhIICwsjNMuGkNWqVWPDhg0A7Nmzh/j4eCpVqkRoaChhYWEkJCQQGRnJ/v37ufrqq11Zbv4GDYL9+9n511+wf79bhDMwMzfPn4dx46yuRERERJzBpV2cfn5+TJw4keHDh5OcnEy/fv2oX78+s2bNolmzZnTq1Ilx48bx3HPPsXDhQmw2G9OnT8dms1G/fn26detG9+7d8fX1ZeLEidbN4HRj58/DrFnQqxdYnV9FRETEOVw+Bi0kJISQkJBMx0aNGpV2u169eixZsiTH1z788MM8/PDDLq3P082fb7o4n33W6kpERETEWbSTgAeLj4fXXjNrnLVrZ3U1IiIi4iyWz+KUwvvPf+DwYVi0yOpKRERExJnUguahkpLMXptt2kCnTlZXIyIiIs6kFjQPtWwZ7N0Lr78ONlv+zxcRERHPoRY0D5SSAtOmQZMmcNttVlcjIiIizqYWNA/01VewfTt8+KHZe1NERES8i77ePYzDAVOmQO3acOedVlcjIiIirqAWNA8THg6bNpmtnfz0pyciIuKV1ILmYaZOhcBAuO8+qysRERERV1FA8yAbN8KaNfD001CypNXViIiIiKsooHmQadOgYkV48EGrKxERERFXUkDzENu3w8qVMHIkXHGF1dWIiIiIKymgeYjp06FMGXj8casrEREREVdTQPMAe/fCJ5/AQw9B5cpWVyMiIiKupoDmAWbMMEtqPPWU1ZWIiIhIUVBAc3OHDsGCBTB0KFSvbnU1IiIiUhQU0NzczJmQlARjx1pdiYiIiBQVBTQ3duIEzJ0Ld90FdetaXY2IiIgUFQU0N/bWWxATA+PGWV2JiIiIFCUFNDd17hy8+Sb07g3NmlldjYiIiBQlBTQ3NW8enDoF48dbXYmIiIgUNQU0NxQXB6+/Dp06Qdu2VlcjIiIiRc3P6gIku4UL4cgRWLzY6kpERETECmpBczNJSfDKK6blrGNHq6sRERERK6gFzc0sWQL798OsWWCzWV2NiIiIWEEtaG4kJQWmTTOzNnv2tLoaERERsYpa0NzIl1/Cjh1m7JmPorOIiEixpRjgJhwOmDrV7BgwcKDV1YiIiIiV1ILmJtasgc2bzfpnfvpTERERKdbUguYmpk6FatVgyBCrKxERERGrqa3GDWzYAOHhZnHagACrqxERERGrqQXNDUybBpUqwYgRVlciIiIi7kABzWLbtsGqVTBqFJQta3U1IiIi4g4U0Cw2fboJZo89ZnUlIiIi4i4U0Cy0ezcsXQoPP2y6OEVERERAAc1SM2ZAiRLw5JNWVyIiIiLuRAHNIgcPwsKFcP/9ZnkNERERkVQKaBZ5/XWz9+aYMVZXIiIiIu7G5eugrVu3jilTppCSksKAAQMYkWUtialTp7Jx40YA4uLiOHHiBL/++isAjRs3pkGDBgBUq1aNuXPnurrcInH8uNkx4O67oU4dq6sRERERd+PSgJacnMzkyZNZsGABdrud/v37ExoaSr169dKe8+yzz6bd/vDDD9mxY0fa/ZIlS7Jy5UpXlmiJN9+ECxdg3DirKxERERF35NIuzm3bthEcHExQUBD+/v706NGDNWvW5Pr8sLAwevbs6cqSLHf2LLz1FvTtC02aWF2NiIiIuCOXBrTo6GgCAwPT7tvtdqKjo3N87sGDB4mKiqJdu3Zpx+Lj47n99tsZOHAg33//vStLLTJz58Lp0zB+vNWViIiIiLtym704w8LC6NKlC76+vmnHwsPDsdvtREZGMmTIEBo0aECtWrVyfY/4+HgiIiJcXmtcXFyhPicuzsaMGfW44YY4ypaNpAhK9QiFPZ+SO51T59L5dD6dU+fS+XQ+q8+pSwOa3W7nyJEjafejo6Ox2+05Pvfrr79m4sSJ2V4PEBQUxHXXXceOHTvyDGgBAQE0btzYCZXnLSIiolCf8847cOIETJlStkjq9BSFPZ+SO51T59L5dD6dU+fS+XS+ojineQVAl3ZxNm/enP379xMZGUlCQgJhYWGEhoZme96ePXs4e/YsLVu2TDt25swZEhISADh58iS//fZbpskFniYx0SxMe/31EBJidTUiIiLizlzagubn58fEiRMZPnw4ycnJ9OvXj/r16zNr1iyaNWtGp06dANN61r17d2w2W9pr9+zZw6RJk7DZbDgcDh544AGPDmiffAL//guzZ0OGH1NEREQkG5ePQQsJCSEkS5PRqFGjMt1//PHHs72uVatWrFq1yqW1FZWUFJg2Da6+Gnr0sLoaERERcXduM0nAm33xBezcaVrR1HomIiIi+dFWTy7mcMDUqVCvHgwYYHU1IiIi4gnUguZi330HW7bAu+9ChhVERERERHKlFjQXmzoVatSAe++1uhIRERHxFGpBc6FffoEff4Q33oCAAKurEREREU+hFjQXmjYNKleGBx6wuhIRERHxJApoLvL77xAWBk88AWXKWF2NiIiIeBIFNBeZPh2uuAIefdTqSkRERMTTKKC5wK5dsGwZPPIIVKxodTUiIiLiaRTQXGDGDDMp4Mknra5EREREPJECmpNFRsKiRTBsGNjtVlcjIiIinkgBzclef93sHjBmjNWViIiIiKdSQHOiY8dg/nwYNAiCg62uRkRERDyVApoTzZoFcXHwzDNWVyIiIiKeTAHN3b3EdAAAC7dJREFUSc6cgdmz4fbboXFjq6sRERERT6aA5iRz5piQNn681ZWIiIiIp1NAc4ILF2DmTOjSBa691upqRERExNMpoDnBBx+YCQLPPmt1JSIiIuINFNAuU0KCWZi2fXu48UarqxERERFv4Gd1AZ7u44/N4rRz54LNZnU1IiIi4g3UgnYZkpPNpujXXAPdulldjYiIiHgLtaBdhs8/h7//hqVL1XomIiIizqMWtEJyOGDqVKhfH/r1s7oaERER8SZqQSukb7+FrVvh/ffB19fqakRERMSbqAWtkKZOhZo14Z57rK5EREREvI1a0Arh119L8dNPZu9Nf3+rqxERERFvoxa0ApoxA8LDze13372SK680489mzLC2LhEREfE+CmgF1KYNDBwI8+fDTz+VpXdvGDzYHBcRERFxJnVxFlDHjrBsGXTtCiVKpPDFFz4sX26Oi4iIiDiTWtAuQceO4OcHiYk+PPKIwpmIiIi4hgLaJQgPh1Kl4KGHjjFnTvqYNBERERFnUkAroPBwMwZt+XIYOfI4y5aZ+wppIiIi4mwKaAW0ebMZg5barZk6Jm3zZmvrEhEREe+jSQIFNHZs9mMdO2ocmoiIiDifWtBERERE3IwCmoiIiIibUUATERERcTMKaCIiIiJuRgFNRERExM0ooImIiIi4GQU0ERERETejgCYiIiLiZmwOh8NhdRHO8vvvvxMQEGB1GSIiIiL5io+Pp0WLFjk+5lUBTURERMQbqItTRERExM0ooImIiIi4GQU0ERERETejgCYiIiLiZhTQRERERNyMAloBHT58mHvvvZfu3bvTo0cP/vOf/1hdktdITk6mT58+PPjgg1aX4vHOnj3LyJEj6dq1K926dWPr1q1Wl+TxFi5cSI8ePejZsydPPfUU8fHxVpfkccaPH8/1119Pz549046dPn2aoUOH0rlzZ4YOHcqZM2csrNCz5HQ+X3nlFbp27UqvXr149NFHOXv2rIUVep6czmmqDz74gIYNG3Ly5MkirUkBrYB8fX0ZN24cX3/9NUuXLuXjjz9m9+7dVpflFRYtWsRVV11ldRleYcqUKdx4443897//ZeXKlTqvlyk6OppFixbx2Wef8dVXX5GcnExYWJjVZXmc22+/nffeey/Tsfnz53P99dezevVqrr/+eubPn29RdZ4np/PZvn17vvrqK1atWkXt2rWZN2+eRdV5ppzOKZjGmV9++YXq1asXeU0KaAVUtWpVmjZtCkDZsmWpW7cu0dHRFlfl+Y4cOcIPP/xA//79rS7F4507d47NmzennUt/f3/KlStncVWeLzk5mbi4OJKSkoiLi6Nq1apWl+Rx2rRpQ/ny5TMdW7NmDX369AGgT58+fP/991aU5pFyOp8dOnTAz88PgBYtWnDkyBErSvNYOZ1TgGnTpjFmzBhsNluR16SAVghRUVFERERwzTXXWF2Kx5s6dSpjxozBx0d/FS9XVFQUlSpVYvz48fTp04cJEyZw4cIFq8vyaHa7nfvvv5+OHTvSoUMHypYtS4cOHawuyyucOHEiLexWqVKFEydOWFyR9/jss8+46aabrC7D433//fdUrVqVRo0aWfL5+la8RDExMYwcOZJnn32WsmXLWl2ORwsPD6dSpUo0a9bM6lK8QlJSEjt27OCuu+7iiy++oFSpUuo2ukxnzpxhzZo1rFmzhp9++onY2FhWrlxpdVlex2azWdJC4Y3mzJmDr68vt912m9WleLTY2FjmzZvHqFGjLKtBAe0SJCYmMnLkSHr16kXnzp2tLsfj/fbbb6xdu5bQ0FCeeuop/ve//zF69Giry/JYgYGBBAYGprXsdu3alR07dlhclWdbv349NWvWpFKlSpQoUYLOnTtr4oWTVK5cmaNHjwJw9OhRKlWqZHFFnm/FihX88MMPvPbaawq8l+nAgQNERUXRu3dvQkNDOXLkCLfffjvHjh0rshoU0ArI4XAwYcIE6taty9ChQ60uxys8/fTTrFu3jrVr1zJz5kzatWvHa6+9ZnVZHqtKlSoEBgayd+9eADZs2KBJApepevXq/PHHH8TGxuJwOHROnSg0NJQvvvgCgC+++IJOnTpZXJFnW7duHe+99x5z5syhVKlSVpfj8Ro2bMiGDRtYu3Yta9euJTAwkBUrVlClSpUiq8GvyD7Jw23ZsoWVK1fSoEEDevfuDcBTTz1FSEiIxZWJpHv++ecZPXo0iYmJBAUFMW3aNKtL8mjXXHMNXbp0oW/fvvj5+dG4cWPuuOMOq8vyOE899RSbNm3i1KlT3HTTTTz++OOMGDGCJ554gk8//ZTq1avzf//3f1aX6TFyOp/z588nISEhrQHhmmuuYfLkyRZX6jlyOqcDBgywtCabw+FwWFqBiIiIiGSiLk4RERERN6OAJiIiIuJmFNBERERE3IwCmoiIiIibUUATERERcTMKaCJiuYYNG2ZapDgpKYl27drx4IMPFur91qxZY+kuCvfeey9dunShV69edO3alcmTJ3P27NlCv9+KFSsy7f0bGhrKyZMnnVGqiLgpBTQRsVzp0qX5559/iIuLA+CXX37BbrcX+v06derEiBEjnFVeobz22musWrXq/9u7m5CoujiO49+rOIiufFkYrhTBCWYjIpIbSUFMs3FgUCEYQ4TwBQlUnNmMBCYiSgm+7CJCCTdOk02N6KKtK0GEVBhR0XbKkGiZOT0L8UIvZugDXZ/n91ld7rnnnP+9qz/n3Hv/vHr1CpvNRnNz84XHCgQC5l/3ReT/QQmaiFhCcXEx7969AyAUClFZWWm2LS4uUltbS3V1NXV1dWa1hGfPnuHz+QBYWVnh9u3bfPr0iampKfMnnV6vl+7ubmpqaigtLWV+fh6fz8etW7fwer3mHHl5eeZxOBw22/60/1lsNhudnZ18+PCB5eVlAILBIG63G6fTid/v5/j42Iyht7eXyspK6uvr2d3dJRwOs7S0REdHB06n00xix8fHcblcVFVVEYlELvTMRcS6lKCJiCVUVFTw5s0bDg8PWVlZMWuKAmRnZzMxMcHLly9pa2vj8ePHAHg8HjY3N5mdncXn8/Hw4cNflrn5+PEjk5OT+Hw+mpqauHfvHqFQiNXVVd6/f39ubJftHx8fj91uZ21tjUgkwtu3b3nx4gXBYJC4uDimp6cBODg4wOFwEAqFKCgoYHh4mPLychwOBwMDAwSDQRITEwFISUkhEAhQV1fH06dP/+gZi8jVoVJPImIJdrudra0tXr9+/VMJtb29Pbq6utjY2MAwDI6OjgCIi4ujr6+PO3fuUFtbS35+/i/HvnnzJoZhkJubS3p6Orm5uQDk5OSwvb3N9evXfxvbZfvDST1fOKmRurS0hNvtBuDz58+kpaWZ91NRUQGA0+mktbX1zPHKysoAcDgczM7Onju/iFwtStBExDJKSkro7+/n+fPnRKNR8/zQ0BCFhYWMjIywtbWFx+Mx29bX10lKSvrtO1o2mw0AwzDMYzhJiL5+/frT9YeHh5fq/6Pj42NWV1fJzs5mZ2cHl8tFe3v7uf0MwzizLSEhwYzhdItURP47tMUpIpbhdrtpaWkxV6hO7e3tmR8NBAKB78739PQwPj5ONBolHA5feO709HQikQixWIy5ubkLj/Ojo6MjBgcHuXbtGna7nRs3bjAzM8POzg4A0WiU7e1tAGKxGDMzMwBMT0+bK4LJycns7+//azGJiPVpBU1ELCMjI+O71bFTjY2NeL1exsbGvtv+7O3t5e7du2RlZfHo0SM8Hg8FBQUXmru9vZ379++TmpqKw+Hg4ODgwvcB0NHRgc1m48uXLxQVFTE6OgqcbIs+ePCAhoYGYrEYCQkJ+P1+MjMzSUpKYnFxkbGxMVJTU3ny5AkALpeL7u5uEhMTmZycvFRcInI1GN9OX4wQEZG/Ki8vj4WFhb8dhohYgLY4RURERCxGK2giIiIiFqMVNBERERGLUYImIiIiYjFK0EREREQsRgmaiIiIiMUoQRMRERGxGCVoIiIiIhbzD24JPt+PU6PGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Tree Regressor - Validation Curev\n",
    "\n",
    "ValidationCurve(DecisionTreeRegressor(random_state=0), X, y, param_name='max_depth', \n",
    "                Name_of_HyperParameter='Maximum Depth', param_range = np.arange(2,15), )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HYiKevpW23vj"
   },
   "source": [
    "## Cross Validation Summary\n",
    "\n",
    "Now that we have the hyperparameters for knn and decision tree we can summarize the cross validation table.\n",
    "The table and the graphs below shows the scores of the R² for each round of testing in CV. Since cv is set to be 10, we have 10 rounds of testings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "executionInfo": {
     "elapsed": 7650,
     "status": "ok",
     "timestamp": 1669753688860,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "RBLGv0zV25z8",
    "outputId": "32d0f021-8a3a-4b89-d4d3-dc858586f929"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-49d360d0-fa59-4282-b36b-869a9cd97b81\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Linear Regression</th>\n",
       "      <th>K-Nearest Neighbors</th>\n",
       "      <th>Decision Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.938738</td>\n",
       "      <td>0.916592</td>\n",
       "      <td>0.937776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.931256</td>\n",
       "      <td>0.915346</td>\n",
       "      <td>0.934679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.844145</td>\n",
       "      <td>0.841111</td>\n",
       "      <td>0.877514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.909687</td>\n",
       "      <td>0.861453</td>\n",
       "      <td>0.917183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.932827</td>\n",
       "      <td>0.935987</td>\n",
       "      <td>0.953179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.639539</td>\n",
       "      <td>0.616568</td>\n",
       "      <td>0.705168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.896386</td>\n",
       "      <td>0.861955</td>\n",
       "      <td>0.908387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.890099</td>\n",
       "      <td>0.912355</td>\n",
       "      <td>0.931939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.781909</td>\n",
       "      <td>0.743892</td>\n",
       "      <td>0.804713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.892075</td>\n",
       "      <td>0.905474</td>\n",
       "      <td>0.916700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>0.865666</td>\n",
       "      <td>0.851073</td>\n",
       "      <td>0.888724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49d360d0-fa59-4282-b36b-869a9cd97b81')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-49d360d0-fa59-4282-b36b-869a9cd97b81 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-49d360d0-fa59-4282-b36b-869a9cd97b81');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      Linear Regression  K-Nearest Neighbors  Decision Tree\n",
       "1              0.938738             0.916592       0.937776\n",
       "2              0.931256             0.915346       0.934679\n",
       "3              0.844145             0.841111       0.877514\n",
       "4              0.909687             0.861453       0.917183\n",
       "5              0.932827             0.935987       0.953179\n",
       "6              0.639539             0.616568       0.705168\n",
       "7              0.896386             0.861955       0.908387\n",
       "8              0.890099             0.912355       0.931939\n",
       "9              0.781909             0.743892       0.804713\n",
       "10             0.892075             0.905474       0.916700\n",
       "Mean           0.865666             0.851073       0.888724"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table - with same cv=10\n",
    "lr_scores = cross_validate(LinearRegression(), X, y, cv=10, scoring='r2')\n",
    "knn_scores = cross_validate(KNeighborsRegressor(n_neighbors=16), X, y, cv=10, scoring='r2')\n",
    "dt_scores = cross_validate(DecisionTreeRegressor(max_depth=9, random_state=0), X, y, cv=10, scoring='r2')\n",
    "\n",
    "lr_test_score = lr_scores.get('test_score')\n",
    "knn_test_score = knn_scores.get('test_score')\n",
    "dt_test_score = dt_scores.get('test_score')\n",
    "\n",
    "box= pd.DataFrame({'Linear Regression':lr_test_score, 'K-Nearest Neighbors':knn_test_score, 'Decision Tree':dt_test_score})\n",
    "box.index = box.index + 1\n",
    "box.loc['Mean'] = box.mean()\n",
    "box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nszeuJr_4AZP"
   },
   "source": [
    "According to the result in the table, the best machine learning model in our analysis is the DECISION TREE since the mean of the scores for each round is the highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 405
    },
    "executionInfo": {
     "elapsed": 1195,
     "status": "ok",
     "timestamp": 1668659849649,
     "user": {
      "displayName": "Tejasri Pavuluri",
      "userId": "17831795534061040396"
     },
     "user_tz": 300
    },
    "id": "TyFOwz_u4IhJ",
    "outputId": "b275958f-4a8d-41c0-a0a8-9b4838c966f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'K-th Fold')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2, figsize=(14,6))\n",
    "\n",
    "sns.boxplot(data=box.drop(box.tail(1).index), width=0.3, palette=\"Set2\", ax=ax[0])\n",
    "ax[0].set_ylabel('R squared')\n",
    "\n",
    "sns.lineplot(data=box.drop(box.tail(1).index), palette=\"Set2\", ax=ax[1])\n",
    "ax[1].set_xticks(np.arange(1,11,1))\n",
    "ax[1].set_xlabel('K-th Fold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GhKHqNShGLLb"
   },
   "source": [
    "The box and line plots above show the distributions and the changes of the scores for each model. The decision tree model as well as Linear Regression shows a good performance in this analysis. KNN did not show a significant difference in their performance."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyN+GPQKwvzy4/ZH0ZizuFJo",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}