Predicting_Monthly_Rainfall.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf6fbff0",
   "metadata": {},
   "source": [
    "# Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3674ad36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>Year</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kitgum</td>\n",
       "      <td>32.8830</td>\n",
       "      <td>3.300</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.70</td>\n",
       "      <td>267.60</td>\n",
       "      <td>110.00</td>\n",
       "      <td>84.70</td>\n",
       "      <td>140.00</td>\n",
       "      <td>175.60</td>\n",
       "      <td>161.6</td>\n",
       "      <td>220.1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lira</td>\n",
       "      <td>32.9000</td>\n",
       "      <td>2.250</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.40</td>\n",
       "      <td>23.90</td>\n",
       "      <td>319.50</td>\n",
       "      <td>135.50</td>\n",
       "      <td>86.30</td>\n",
       "      <td>99.70</td>\n",
       "      <td>255.30</td>\n",
       "      <td>278.9</td>\n",
       "      <td>196.1</td>\n",
       "      <td>147.8</td>\n",
       "      <td>93.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kasese</td>\n",
       "      <td>30.1000</td>\n",
       "      <td>0.183</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.70</td>\n",
       "      <td>22.80</td>\n",
       "      <td>18.50</td>\n",
       "      <td>302.80</td>\n",
       "      <td>124.10</td>\n",
       "      <td>13.80</td>\n",
       "      <td>22.90</td>\n",
       "      <td>36.60</td>\n",
       "      <td>51.3</td>\n",
       "      <td>276.9</td>\n",
       "      <td>93.2</td>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gulu</td>\n",
       "      <td>32.2830</td>\n",
       "      <td>2.783</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.60</td>\n",
       "      <td>10.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>218.80</td>\n",
       "      <td>201.40</td>\n",
       "      <td>106.00</td>\n",
       "      <td>198.60</td>\n",
       "      <td>401.7</td>\n",
       "      <td>214.7</td>\n",
       "      <td>111.4</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arua</td>\n",
       "      <td>30.9170</td>\n",
       "      <td>3.050</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.30</td>\n",
       "      <td>18.30</td>\n",
       "      <td>142.80</td>\n",
       "      <td>110.80</td>\n",
       "      <td>109.10</td>\n",
       "      <td>355.50</td>\n",
       "      <td>181.70</td>\n",
       "      <td>226.0</td>\n",
       "      <td>336.7</td>\n",
       "      <td>56.4</td>\n",
       "      <td>47.2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Mubende</td>\n",
       "      <td>31.3666</td>\n",
       "      <td>0.580</td>\n",
       "      <td>2018</td>\n",
       "      <td>7.73</td>\n",
       "      <td>72.50</td>\n",
       "      <td>194.88</td>\n",
       "      <td>125.25</td>\n",
       "      <td>134.52</td>\n",
       "      <td>40.36</td>\n",
       "      <td>36.03</td>\n",
       "      <td>51.96</td>\n",
       "      <td>92.9</td>\n",
       "      <td>142.2</td>\n",
       "      <td>71.2</td>\n",
       "      <td>173.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Kibanda</td>\n",
       "      <td>31.2200</td>\n",
       "      <td>-0.520</td>\n",
       "      <td>2018</td>\n",
       "      <td>28.30</td>\n",
       "      <td>71.60</td>\n",
       "      <td>121.50</td>\n",
       "      <td>186.40</td>\n",
       "      <td>67.80</td>\n",
       "      <td>23.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>42.50</td>\n",
       "      <td>48.2</td>\n",
       "      <td>88.3</td>\n",
       "      <td>142.0</td>\n",
       "      <td>165.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Buginyanya</td>\n",
       "      <td>34.3670</td>\n",
       "      <td>1.283</td>\n",
       "      <td>2018</td>\n",
       "      <td>22.30</td>\n",
       "      <td>41.20</td>\n",
       "      <td>464.90</td>\n",
       "      <td>371.00</td>\n",
       "      <td>295.70</td>\n",
       "      <td>325.40</td>\n",
       "      <td>151.90</td>\n",
       "      <td>166.70</td>\n",
       "      <td>243.6</td>\n",
       "      <td>167.5</td>\n",
       "      <td>79.4</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Kyembogo</td>\n",
       "      <td>30.3330</td>\n",
       "      <td>0.683</td>\n",
       "      <td>2018</td>\n",
       "      <td>18.40</td>\n",
       "      <td>48.53</td>\n",
       "      <td>177.93</td>\n",
       "      <td>183.00</td>\n",
       "      <td>125.40</td>\n",
       "      <td>124.80</td>\n",
       "      <td>49.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>155.6</td>\n",
       "      <td>177.3</td>\n",
       "      <td>211.8</td>\n",
       "      <td>153.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Wadelai</td>\n",
       "      <td>31.4000</td>\n",
       "      <td>2.733</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>254.70</td>\n",
       "      <td>47.50</td>\n",
       "      <td>38.90</td>\n",
       "      <td>28.00</td>\n",
       "      <td>66.50</td>\n",
       "      <td>107.1</td>\n",
       "      <td>99.3</td>\n",
       "      <td>124.9</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Station      LON    LAT  Year    Jan    Feb     Mar     Apr     May  \\\n",
       "0       Kitgum   32.8830  3.300  2012   0.00   0.90    8.70  267.60  110.00   \n",
       "1          Lira  32.9000  2.250  2012   0.00   7.40   23.90  319.50  135.50   \n",
       "2       Kasese   30.1000  0.183  2012   0.70  22.80   18.50  302.80  124.10   \n",
       "3          Gulu  32.2830  2.783  2012   0.00  13.60   10.00  217.00  218.80   \n",
       "4          Arua  30.9170  3.050  2012   0.00   4.30   18.30  142.80  110.80   \n",
       "..          ...      ...    ...   ...    ...    ...     ...     ...     ...   \n",
       "128     Mubende  31.3666  0.580  2018   7.73  72.50  194.88  125.25  134.52   \n",
       "129     Kibanda  31.2200 -0.520  2018  28.30  71.60  121.50  186.40   67.80   \n",
       "130  Buginyanya  34.3670  1.283  2018  22.30  41.20  464.90  371.00  295.70   \n",
       "131    Kyembogo  30.3330  0.683  2018  18.40  48.53  177.93  183.00  125.40   \n",
       "132     Wadelai  31.4000  2.733  2018   0.00   0.00    0.00  254.70   47.50   \n",
       "\n",
       "        Jun     Jul     Aug    Sep    Oct    Nov    Dec  \n",
       "0     84.70  140.00  175.60  161.6  220.1   63.1   30.3  \n",
       "1     86.30   99.70  255.30  278.9  196.1  147.8   93.6  \n",
       "2     13.80   22.90   36.60   51.3  276.9   93.2  123.1  \n",
       "3    201.40  106.00  198.60  401.7  214.7  111.4   52.6  \n",
       "4    109.10  355.50  181.70  226.0  336.7   56.4   47.2  \n",
       "..      ...     ...     ...    ...    ...    ...    ...  \n",
       "128   40.36   36.03   51.96   92.9  142.2   71.2  173.8  \n",
       "129   23.60    0.60   42.50   48.2   88.3  142.0  165.6  \n",
       "130  325.40  151.90  166.70  243.6  167.5   79.4   38.2  \n",
       "131  124.80   49.80    0.00  155.6  177.3  211.8  153.8  \n",
       "132   38.90   28.00   66.50  107.1   99.3  124.9   40.4  \n",
       "\n",
       "[133 rows x 16 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "file_path = \"Climate Data RF & Temp 2012 to 2018.xlsx\"\n",
    "df = pd.read_excel(file_path)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e358d72a",
   "metadata": {},
   "source": [
    "## Checking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e746b213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values:\n",
      " Station    0\n",
      "LON        0\n",
      "LAT        0\n",
      "Year       0\n",
      "Jan        0\n",
      "Feb        0\n",
      "Mar        0\n",
      "Apr        1\n",
      "May        0\n",
      "Jun        0\n",
      "Jul        0\n",
      "Aug        0\n",
      "Sep        0\n",
      "Oct        0\n",
      "Nov        0\n",
      "Dec        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "missing_values = df.isna().sum()\n",
    "print(\"Missing values:\\n\", missing_values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6827857c",
   "metadata": {},
   "source": [
    "## Dropping unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9bd40e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = df.drop(columns=['LON', 'LAT', 'Year'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e3a0bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kitgum</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.70</td>\n",
       "      <td>267.60</td>\n",
       "      <td>110.00</td>\n",
       "      <td>84.70</td>\n",
       "      <td>140.00</td>\n",
       "      <td>175.60</td>\n",
       "      <td>161.6</td>\n",
       "      <td>220.1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lira</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.40</td>\n",
       "      <td>23.90</td>\n",
       "      <td>319.50</td>\n",
       "      <td>135.50</td>\n",
       "      <td>86.30</td>\n",
       "      <td>99.70</td>\n",
       "      <td>255.30</td>\n",
       "      <td>278.9</td>\n",
       "      <td>196.1</td>\n",
       "      <td>147.8</td>\n",
       "      <td>93.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kasese</td>\n",
       "      <td>0.70</td>\n",
       "      <td>22.80</td>\n",
       "      <td>18.50</td>\n",
       "      <td>302.80</td>\n",
       "      <td>124.10</td>\n",
       "      <td>13.80</td>\n",
       "      <td>22.90</td>\n",
       "      <td>36.60</td>\n",
       "      <td>51.3</td>\n",
       "      <td>276.9</td>\n",
       "      <td>93.2</td>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gulu</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.60</td>\n",
       "      <td>10.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>218.80</td>\n",
       "      <td>201.40</td>\n",
       "      <td>106.00</td>\n",
       "      <td>198.60</td>\n",
       "      <td>401.7</td>\n",
       "      <td>214.7</td>\n",
       "      <td>111.4</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arua</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.30</td>\n",
       "      <td>18.30</td>\n",
       "      <td>142.80</td>\n",
       "      <td>110.80</td>\n",
       "      <td>109.10</td>\n",
       "      <td>355.50</td>\n",
       "      <td>181.70</td>\n",
       "      <td>226.0</td>\n",
       "      <td>336.7</td>\n",
       "      <td>56.4</td>\n",
       "      <td>47.2</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Mubende</td>\n",
       "      <td>7.73</td>\n",
       "      <td>72.50</td>\n",
       "      <td>194.88</td>\n",
       "      <td>125.25</td>\n",
       "      <td>134.52</td>\n",
       "      <td>40.36</td>\n",
       "      <td>36.03</td>\n",
       "      <td>51.96</td>\n",
       "      <td>92.9</td>\n",
       "      <td>142.2</td>\n",
       "      <td>71.2</td>\n",
       "      <td>173.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Kibanda</td>\n",
       "      <td>28.30</td>\n",
       "      <td>71.60</td>\n",
       "      <td>121.50</td>\n",
       "      <td>186.40</td>\n",
       "      <td>67.80</td>\n",
       "      <td>23.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>42.50</td>\n",
       "      <td>48.2</td>\n",
       "      <td>88.3</td>\n",
       "      <td>142.0</td>\n",
       "      <td>165.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Buginyanya</td>\n",
       "      <td>22.30</td>\n",
       "      <td>41.20</td>\n",
       "      <td>464.90</td>\n",
       "      <td>371.00</td>\n",
       "      <td>295.70</td>\n",
       "      <td>325.40</td>\n",
       "      <td>151.90</td>\n",
       "      <td>166.70</td>\n",
       "      <td>243.6</td>\n",
       "      <td>167.5</td>\n",
       "      <td>79.4</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Kyembogo</td>\n",
       "      <td>18.40</td>\n",
       "      <td>48.53</td>\n",
       "      <td>177.93</td>\n",
       "      <td>183.00</td>\n",
       "      <td>125.40</td>\n",
       "      <td>124.80</td>\n",
       "      <td>49.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>155.6</td>\n",
       "      <td>177.3</td>\n",
       "      <td>211.8</td>\n",
       "      <td>153.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Wadelai</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>254.70</td>\n",
       "      <td>47.50</td>\n",
       "      <td>38.90</td>\n",
       "      <td>28.00</td>\n",
       "      <td>66.50</td>\n",
       "      <td>107.1</td>\n",
       "      <td>99.3</td>\n",
       "      <td>124.9</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Station    Jan    Feb     Mar     Apr     May     Jun     Jul     Aug  \\\n",
       "0       Kitgum    0.00   0.90    8.70  267.60  110.00   84.70  140.00  175.60   \n",
       "1          Lira   0.00   7.40   23.90  319.50  135.50   86.30   99.70  255.30   \n",
       "2       Kasese    0.70  22.80   18.50  302.80  124.10   13.80   22.90   36.60   \n",
       "3          Gulu   0.00  13.60   10.00  217.00  218.80  201.40  106.00  198.60   \n",
       "4          Arua   0.00   4.30   18.30  142.80  110.80  109.10  355.50  181.70   \n",
       "..          ...    ...    ...     ...     ...     ...     ...     ...     ...   \n",
       "128     Mubende   7.73  72.50  194.88  125.25  134.52   40.36   36.03   51.96   \n",
       "129     Kibanda  28.30  71.60  121.50  186.40   67.80   23.60    0.60   42.50   \n",
       "130  Buginyanya  22.30  41.20  464.90  371.00  295.70  325.40  151.90  166.70   \n",
       "131    Kyembogo  18.40  48.53  177.93  183.00  125.40  124.80   49.80    0.00   \n",
       "132     Wadelai   0.00   0.00    0.00  254.70   47.50   38.90   28.00   66.50   \n",
       "\n",
       "       Sep    Oct    Nov    Dec  \n",
       "0    161.6  220.1   63.1   30.3  \n",
       "1    278.9  196.1  147.8   93.6  \n",
       "2     51.3  276.9   93.2  123.1  \n",
       "3    401.7  214.7  111.4   52.6  \n",
       "4    226.0  336.7   56.4   47.2  \n",
       "..     ...    ...    ...    ...  \n",
       "128   92.9  142.2   71.2  173.8  \n",
       "129   48.2   88.3  142.0  165.6  \n",
       "130  243.6  167.5   79.4   38.2  \n",
       "131  155.6  177.3  211.8  153.8  \n",
       "132  107.1   99.3  124.9   40.4  \n",
       "\n",
       "[133 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db130e81",
   "metadata": {},
   "source": [
    "## Imputation of missing value with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6ca9d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputation of missing value with mode\n",
    "\n",
    "# Identify the row with missing value\n",
    "missing_row = df[df['Apr'].isna()]\n",
    "\n",
    "# Get the station and month for the missing value\n",
    "missing_station = missing_row['Station'].values[0]\n",
    "\n",
    "# Calculate mode for April rainfall for the specific station\n",
    "mode_rainfall = df[df['Station'] == missing_station]['Apr'].mode()[0]\n",
    "\n",
    "# Impute the missing value with the mode\n",
    "df.loc[missing_row.index, 'Apr'] = mode_rainfall\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2eb3c16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values:\n",
      " Station    0\n",
      "Jan        0\n",
      "Feb        0\n",
      "Mar        0\n",
      "Apr        0\n",
      "May        0\n",
      "Jun        0\n",
      "Jul        0\n",
      "Aug        0\n",
      "Sep        0\n",
      "Oct        0\n",
      "Nov        0\n",
      "Dec        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Confirming that no more missing values\n",
    "missing_values = df.isna().sum()\n",
    "print(\"Missing values:\\n\", missing_values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa53109",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07a66e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kitgum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lira</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kasese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gulu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Mubende</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Kibanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Buginyanya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Kyembogo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Wadelai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Station\n",
       "0       Kitgum \n",
       "1          Lira\n",
       "2       Kasese \n",
       "3          Gulu\n",
       "4          Arua\n",
       "..          ...\n",
       "128     Mubende\n",
       "129     Kibanda\n",
       "130  Buginyanya\n",
       "131    Kyembogo\n",
       "132     Wadelai\n",
       "\n",
       "[133 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Define features (X) and labels (y)\n",
    "X = df[['Station']]\n",
    "y = df[['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c047b85",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.70</td>\n",
       "      <td>267.60</td>\n",
       "      <td>110.00</td>\n",
       "      <td>84.70</td>\n",
       "      <td>140.00</td>\n",
       "      <td>175.60</td>\n",
       "      <td>161.6</td>\n",
       "      <td>220.1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>7.40</td>\n",
       "      <td>23.90</td>\n",
       "      <td>319.50</td>\n",
       "      <td>135.50</td>\n",
       "      <td>86.30</td>\n",
       "      <td>99.70</td>\n",
       "      <td>255.30</td>\n",
       "      <td>278.9</td>\n",
       "      <td>196.1</td>\n",
       "      <td>147.8</td>\n",
       "      <td>93.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.70</td>\n",
       "      <td>22.80</td>\n",
       "      <td>18.50</td>\n",
       "      <td>302.80</td>\n",
       "      <td>124.10</td>\n",
       "      <td>13.80</td>\n",
       "      <td>22.90</td>\n",
       "      <td>36.60</td>\n",
       "      <td>51.3</td>\n",
       "      <td>276.9</td>\n",
       "      <td>93.2</td>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>13.60</td>\n",
       "      <td>10.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>218.80</td>\n",
       "      <td>201.40</td>\n",
       "      <td>106.00</td>\n",
       "      <td>198.60</td>\n",
       "      <td>401.7</td>\n",
       "      <td>214.7</td>\n",
       "      <td>111.4</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>4.30</td>\n",
       "      <td>18.30</td>\n",
       "      <td>142.80</td>\n",
       "      <td>110.80</td>\n",
       "      <td>109.10</td>\n",
       "      <td>355.50</td>\n",
       "      <td>181.70</td>\n",
       "      <td>226.0</td>\n",
       "      <td>336.7</td>\n",
       "      <td>56.4</td>\n",
       "      <td>47.2</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>128</th>\n",
       "      <td>7.73</td>\n",
       "      <td>72.50</td>\n",
       "      <td>194.88</td>\n",
       "      <td>125.25</td>\n",
       "      <td>134.52</td>\n",
       "      <td>40.36</td>\n",
       "      <td>36.03</td>\n",
       "      <td>51.96</td>\n",
       "      <td>92.9</td>\n",
       "      <td>142.2</td>\n",
       "      <td>71.2</td>\n",
       "      <td>173.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>28.30</td>\n",
       "      <td>71.60</td>\n",
       "      <td>121.50</td>\n",
       "      <td>186.40</td>\n",
       "      <td>67.80</td>\n",
       "      <td>23.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>42.50</td>\n",
       "      <td>48.2</td>\n",
       "      <td>88.3</td>\n",
       "      <td>142.0</td>\n",
       "      <td>165.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>22.30</td>\n",
       "      <td>41.20</td>\n",
       "      <td>464.90</td>\n",
       "      <td>371.00</td>\n",
       "      <td>295.70</td>\n",
       "      <td>325.40</td>\n",
       "      <td>151.90</td>\n",
       "      <td>166.70</td>\n",
       "      <td>243.6</td>\n",
       "      <td>167.5</td>\n",
       "      <td>79.4</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>18.40</td>\n",
       "      <td>48.53</td>\n",
       "      <td>177.93</td>\n",
       "      <td>183.00</td>\n",
       "      <td>125.40</td>\n",
       "      <td>124.80</td>\n",
       "      <td>49.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>155.6</td>\n",
       "      <td>177.3</td>\n",
       "      <td>211.8</td>\n",
       "      <td>153.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>254.70</td>\n",
       "      <td>47.50</td>\n",
       "      <td>38.90</td>\n",
       "      <td>28.00</td>\n",
       "      <td>66.50</td>\n",
       "      <td>107.1</td>\n",
       "      <td>99.3</td>\n",
       "      <td>124.9</td>\n",
       "      <td>40.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Jan    Feb     Mar     Apr     May     Jun     Jul     Aug    Sep  \\\n",
       "0     0.00   0.90    8.70  267.60  110.00   84.70  140.00  175.60  161.6   \n",
       "1     0.00   7.40   23.90  319.50  135.50   86.30   99.70  255.30  278.9   \n",
       "2     0.70  22.80   18.50  302.80  124.10   13.80   22.90   36.60   51.3   \n",
       "3     0.00  13.60   10.00  217.00  218.80  201.40  106.00  198.60  401.7   \n",
       "4     0.00   4.30   18.30  142.80  110.80  109.10  355.50  181.70  226.0   \n",
       "..     ...    ...     ...     ...     ...     ...     ...     ...    ...   \n",
       "128   7.73  72.50  194.88  125.25  134.52   40.36   36.03   51.96   92.9   \n",
       "129  28.30  71.60  121.50  186.40   67.80   23.60    0.60   42.50   48.2   \n",
       "130  22.30  41.20  464.90  371.00  295.70  325.40  151.90  166.70  243.6   \n",
       "131  18.40  48.53  177.93  183.00  125.40  124.80   49.80    0.00  155.6   \n",
       "132   0.00   0.00    0.00  254.70   47.50   38.90   28.00   66.50  107.1   \n",
       "\n",
       "       Oct    Nov    Dec  \n",
       "0    220.1   63.1   30.3  \n",
       "1    196.1  147.8   93.6  \n",
       "2    276.9   93.2  123.1  \n",
       "3    214.7  111.4   52.6  \n",
       "4    336.7   56.4   47.2  \n",
       "..     ...    ...    ...  \n",
       "128  142.2   71.2  173.8  \n",
       "129   88.3  142.0  165.6  \n",
       "130  167.5   79.4   38.2  \n",
       "131  177.3  211.8  153.8  \n",
       "132   99.3  124.9   40.4  \n",
       "\n",
       "[133 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a575da",
   "metadata": {},
   "source": [
    "## Standardisation of label values to ensure they are all on the same scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ae60217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-8.04334666e-01 -1.02048163e+00 -1.48978504e+00  1.07893500e+00\n",
      "  -4.27465195e-01 -1.06704103e-01  7.23959043e-01  6.70439283e-01\n",
      "   3.00098780e-01  8.03911504e-01 -1.03276693e+00 -5.25142647e-01]\n",
      " [-8.04334666e-01 -8.37718207e-01 -1.27542641e+00  1.74610792e+00\n",
      "  -1.18681607e-01 -8.27327411e-02  1.86081877e-01  1.67721135e+00\n",
      "   1.88713723e+00  5.09223348e-01  1.37357819e-01  3.81737725e-01]\n",
      " [-7.81255579e-01 -4.04709472e-01 -1.35158013e+00  1.53142993e+00\n",
      "  -2.56726035e-01 -1.16893509e+00 -8.38954510e-01 -1.08541162e+00\n",
      "  -1.19223150e+00  1.50134014e+00 -6.16937473e-01  8.04375497e-01]\n",
      " [-8.04334666e-01 -6.63390015e-01 -1.47145173e+00  4.28473536e-01\n",
      "   8.90011447e-01  1.64170713e+00  2.70166893e-01  9.60975763e-01\n",
      "   3.54858926e+00  7.37606669e-01 -3.65505709e-01 -2.05657145e-01]\n",
      " [-8.04334666e-01 -9.24882303e-01 -1.35440064e+00 -5.25365212e-01\n",
      "  -4.17777867e-01  2.58859171e-01  3.60020047e+00  7.47494611e-01\n",
      "   1.17141401e+00  2.23560479e+00 -1.12532697e+00 -2.83021347e-01]\n",
      " [-8.04334666e-01 -6.01531625e-01 -8.46709155e-01  6.93285911e-01\n",
      "   3.07560836e-01 -9.62166324e-02  7.90693183e-01  1.05787951e-01\n",
      "   1.93584430e+00 -3.31865762e-01 -9.19700539e-02  1.54649877e+00]\n",
      " [-8.04334666e-01 -9.52999753e-01 -1.47427224e+00  1.55713987e+00\n",
      "  -1.84071073e-01  2.03425396e-01  7.42644602e-01  1.06708474e+00\n",
      "   5.51751936e-01 -1.12875165e+00 -2.08015483e-01  5.79446242e-01]\n",
      " [-8.04334666e-01 -7.30871896e-01 -6.54283690e-02  3.30775767e-01\n",
      "   2.93524863e+00  9.66014359e-01 -1.15556435e-01 -1.83485327e-01\n",
      "  -4.88685039e-01  5.76756050e-01  3.00374017e-01  6.42483740e-01]\n",
      " [-5.93325877e-01  9.75857338e-01  3.40724819e-01 -9.74003652e-01\n",
      "  -7.78630844e-01  9.52530467e-01  1.66061635e-01  6.08542381e-01\n",
      "  -9.25695627e-01 -1.52597134e-01  6.82831583e-02  1.05652549e+00]\n",
      " [-7.71364542e-01  1.46392554e-01 -9.83503805e-01  3.15349803e-01\n",
      "  -1.58641836e-01 -1.04907828e+00 -6.86800671e-01  9.69455363e-02\n",
      "  -2.96738725e-03  2.17600245e-02  2.85177592e-01  6.35320388e-01]\n",
      " [-6.46078074e-01  8.45341630e-02 -1.17607772e-01  3.35917755e-01\n",
      "   6.91421218e-01 -2.58023328e-01 -1.13792346e+00 -1.00961949e+00\n",
      "  -1.00010920e+00 -9.28609277e-01  5.53187275e-01  2.74994192e+00]\n",
      " [-7.12018320e-01  4.38814037e-01 -5.27360819e-02  1.71374143e-01\n",
      "   2.05737365e-02 -1.20189572e+00 -1.01913669e+00 -7.56979074e-01\n",
      "  -5.50921841e-01 -4.93944247e-01  5.79435646e-01  8.74576347e-01]\n",
      " [-6.92236246e-01  1.42011305e+00 -1.05119600e+00 -1.52162536e+00\n",
      "  -3.85083134e-01  4.98572794e-01 -2.79722419e-01 -4.87917029e-01\n",
      "  -6.51041914e-01 -8.24240555e-01  1.08368067e+00  2.08948086e+00]\n",
      " [-8.04334666e-01 -9.97987674e-01 -1.57016952e+00 -2.39984886e-01\n",
      "  -7.26667979e-02  6.93340113e-01  1.19243270e+00  6.45175242e-01\n",
      "  -1.14758336e+00 -8.36519228e-01 -1.74561742e+00 -5.38036681e-01]\n",
      " [-8.00048550e-01  4.69743232e-01 -4.64107210e-01 -5.84112423e-01\n",
      "  -7.83474509e-01 -4.01851502e-01 -3.25101634e-01 -1.98264791e-01\n",
      "  -1.12999470e+00  1.03106696e+00  1.58101822e+00  2.47100099e+00]\n",
      " [-8.04334666e-01  2.78544570e-01 -8.09411644e-02 -3.67249085e-01\n",
      "   1.70132519e+00 -1.18391720e+00 -1.06718527e+00 -8.79509676e-01\n",
      "  -1.11511198e+00 -7.14960364e-01 -1.07006725e+00 -2.28579871e-01]\n",
      " [-8.04334666e-01 -6.04343370e-01 -1.37837496e+00  1.86437365e+00\n",
      "  -1.75947283e+00  1.55780737e+00  1.03760950e+00  2.78630278e+00\n",
      "   2.34579541e+00  2.15456555e+00  5.75291166e-01  9.64834583e-01]\n",
      " [-3.88911112e-01 -1.04578734e+00  2.20073868e-02 -1.39950315e+00\n",
      "  -4.88010997e-01 -4.54288857e-01 -6.02715655e-01 -6.74870938e-01\n",
      "   1.21200323e+00  4.65020125e-01  1.09196963e+00  2.70696181e+00]\n",
      " [-7.05424296e-01 -8.77082638e-01 -1.20350345e+00 -3.55679612e-01\n",
      "  -2.09500310e-01 -1.02209473e-01  3.36901032e-01 -4.48757765e-01\n",
      "  -5.33333179e-01  7.29011597e-01 -3.98661546e-01  3.44488294e-01]\n",
      " [ 1.66512757e+00 -9.81117204e-01 -5.56196805e-01 -7.02992863e-02\n",
      "   2.50647783e-01  2.04923606e-01  7.19954995e-01  2.85451569e+00\n",
      "   1.16735509e+00  2.45539304e+00 -8.54554305e-01 -8.03080707e-01]\n",
      " [ 1.44093073e+00 -7.08377936e-01  4.09827271e-01 -7.05334787e-01\n",
      "   4.96357213e-02  2.58859171e-01  1.97856087e+00  1.54331192e+00\n",
      "   1.11053018e+00  1.07527018e+00 -2.95049556e-01 -4.86460546e-01]\n",
      " [-2.40545557e-01  4.30378802e-01 -6.95811963e-01 -7.54412741e-02\n",
      "  -4.40785272e-01 -1.24534381e+00 -1.03248351e+00 -5.34655507e-01\n",
      "  -5.72569424e-01 -8.04594678e-01 -2.14922949e-01 -5.65257419e-01]\n",
      " [ 2.87183408e+00 -1.02329338e+00  3.99955492e-01 -1.18739615e+00\n",
      "  -9.43315425e-01  2.07920027e-01  3.13306149e+00  1.75931948e+00\n",
      "   5.92341155e-01  1.32943871e+00  2.10576959e-01 -1.08139686e-02]\n",
      " [ 4.48530019e-01 -4.58132628e-01 -5.42094264e-01 -7.90177586e-01\n",
      "   5.08466373e-02  9.83992880e-01  9.34838925e-01  1.40562290e+00\n",
      "   1.17141401e+00  5.24567074e-02  1.32682347e+00 -7.48639231e-01]\n",
      " [ 3.82589773e-01 -4.66567863e-01  7.87775377e-01  1.50806192e-01\n",
      "   5.26736638e-01 -6.11600921e-01  6.38539344e-01 -9.26248153e-01\n",
      "   2.36509003e-01 -7.72670128e-01  2.54784742e-01 -5.06517932e-01]\n",
      " [ 1.41785164e+00 -3.70968532e-01  9.16108502e-01 -5.74214096e-01\n",
      "  -6.17685536e-02  8.80632151e-02 -2.74673705e-02  1.53789630e-01\n",
      "  -2.27561064e-01 -4.27639412e-01 -3.94517067e-01  6.05234309e-01]\n",
      " [ 1.53324708e+00 -7.70236326e-01  1.71995335e+00  5.71163699e-01\n",
      "   1.05832878e+00 -7.26963102e-01 -6.40086773e-01  8.20760332e-01\n",
      "   1.21470918e+00 -7.61619322e-01  4.38523338e-01 -1.78436407e-01]\n",
      " [ 1.75084989e+00  3.06662020e-01  1.09239027e+00 -7.15618763e-01\n",
      "  -2.62780615e-01 -2.90983951e-01 -8.09591488e-01  5.30223852e-01\n",
      "   1.08752962e+00 -3.01169079e-01 -3.71031682e-01  1.12395687e-01]\n",
      " [ 1.82338416e+00 -5.34049744e-01  2.64571096e-01  3.28204773e-01\n",
      "  -9.08198860e-01 -1.37568809e+00 -1.13258472e+00  2.56108999e-01\n",
      "  -5.68510502e-01 -5.51654011e-01 -1.09929466e-01 -3.68981572e-01]\n",
      " [ 3.53123655e+00  7.87470421e-01  1.40123592e+00 -6.38718014e-02\n",
      "   6.42984577e-01 -1.12548700e+00 -1.07385868e+00 -9.42669780e-01\n",
      "   8.39935389e-01 -1.02561079e+00  1.71679096e-02  5.96638287e-01]\n",
      " [ 6.10083623e-01 -5.60530885e-02  2.10981440e-01 -1.37379321e+00\n",
      "  -9.05777028e-01 -1.37568809e+00 -1.14326219e+00 -8.71930464e-01\n",
      "   4.84103238e-01 -2.31180642e-01 -5.64440731e-01  2.09817275e-01]\n",
      " [ 2.11681826e+00  1.49040668e+00  9.59826380e-01 -4.18668964e-01\n",
      "  -2.30192406e-02 -1.13297805e+00 -9.76426838e-01 -9.90671460e-01\n",
      "   1.31888817e+00 -6.40060458e-01  5.93250578e-01  7.41337998e-01]\n",
      " [-1.35041163e-01 -9.95175929e-01 -7.08504250e-01 -7.28702802e-02\n",
      "  -1.01839222e+00  5.91461823e-01  1.07764998e+00 -2.92120706e-01\n",
      "  -1.29641049e+00 -1.59656910e+00 -9.16721500e-01 -8.26003434e-01]\n",
      " [ 8.11201376e-01  1.91380474e-01  5.31109126e-01 -3.81389552e-01\n",
      "  -4.03246875e-01 -1.15095657e+00 -5.03949128e-01 -2.08749368e-01\n",
      "   4.00218853e-01  4.14059015e-02  3.74974651e-01  1.43914436e-01]\n",
      " [-1.84496348e-01 -7.78671561e-01  3.66109393e-01 -1.33008631e+00\n",
      "  -5.19494814e-01 -1.36370241e+00 -1.10589107e+00 -1.30268237e+00\n",
      "  -1.34917648e+00 -1.20856302e+00 -9.58166296e-01 -7.80157981e-01]\n",
      " [ 2.55532090e+00 -5.98719879e-01  2.36726000e+00  3.48409982e+00\n",
      "   2.15531218e-01  1.59076799e+00  2.35360674e+00  1.82626919e+00\n",
      "   2.46756306e+00  9.67217856e-01  4.68916189e-01  6.94155745e-02]\n",
      " [ 3.69401723e-01 -4.48061084e-02  1.01200578e+00  1.58290104e-02\n",
      "   8.36731142e-01 -1.18841183e+00 -5.01279763e-01  4.43062908e-01\n",
      "   4.67867550e-01 -1.86977419e-01 -5.71348197e-01 -5.66690089e-01]\n",
      " [ 3.16649526e-01 -5.67790684e-01  2.63160842e-01 -5.09939248e-01\n",
      "  -3.54810233e-01 -5.36690414e-01 -1.01164942e-02  4.38910487e-02\n",
      "   6.57283905e-01 -3.02396946e-01  5.82198632e-01 -7.87321333e-01]\n",
      " [-7.02127283e-01  3.11110074e-02  1.82776357e-01 -1.65403155e+00\n",
      "   1.01594671e+00  2.06420239e+00  3.70268102e-01  1.02160946e+00\n",
      "  -1.55853445e-01  2.63649885e-01 -9.92703626e-01 -9.03367636e-01]\n",
      " [ 1.41907873e-01 -6.32460820e-01 -2.72735726e-01 -9.17441786e-01\n",
      "   1.15520206e+00 -1.29177256e-01  5.69135839e-01  1.97532704e+00\n",
      "   2.12155472e-01  4.23272636e-01  1.30234300e-02 -7.40043209e-01]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler to the original target variable and transform it\n",
    "y_standardized = scaler.fit_transform(y)\n",
    "\n",
    "# Print the first 5 rows of the standardized target variable\n",
    "print(y_standardized[:40])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1958e604",
   "metadata": {},
   "source": [
    "## Find the lowest and highest values, to aid in assessing RMSE values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e8856e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest Value: -1.9625508182850044\n",
      "Highest Value: 6.341646644054006\n"
     ]
    }
   ],
   "source": [
    "# Find the lowest and highest values\n",
    "import numpy as np\n",
    "lowest_value = np.min(y_standardized)\n",
    "highest_value = np.max(y_standardized)\n",
    "\n",
    "print(\"Lowest Value:\", lowest_value)\n",
    "print(\"Highest Value:\", highest_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71f343e",
   "metadata": {},
   "source": [
    "## Encoding categorical Feature values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ecfd0855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Station_Arua', 'Station_Buginyanya', 'Station_Entebbe', 'Station_Gulu',\n",
      "       'Station_Jinja', 'Station_Kabale', 'Station_Kampala', 'Station_Kasese',\n",
      "       'Station_Kibanda', 'Station_Kitgum', 'Station_Kotido',\n",
      "       'Station_Kyembogo', 'Station_Lira', 'Station_Masindi',\n",
      "       'Station_Mbarara', 'Station_Mubende', 'Station_Soroti',\n",
      "       'Station_Tororo', 'Station_Wadelai'],\n",
      "      dtype='object')\n",
      "\n",
      "Encoded Feature Set (X):\n",
      "   Station_Arua  Station_Buginyanya  Station_Entebbe  Station_Gulu  \\\n",
      "0         False               False            False         False   \n",
      "1         False               False            False         False   \n",
      "2         False               False            False         False   \n",
      "3         False               False            False          True   \n",
      "4          True               False            False         False   \n",
      "\n",
      "   Station_Jinja  Station_Kabale  Station_Kampala  Station_Kasese  \\\n",
      "0          False           False            False           False   \n",
      "1          False           False            False           False   \n",
      "2          False           False            False            True   \n",
      "3          False           False            False           False   \n",
      "4          False           False            False           False   \n",
      "\n",
      "   Station_Kibanda  Station_Kitgum  Station_Kotido  Station_Kyembogo  \\\n",
      "0            False            True           False             False   \n",
      "1            False           False           False             False   \n",
      "2            False           False           False             False   \n",
      "3            False           False           False             False   \n",
      "4            False           False           False             False   \n",
      "\n",
      "   Station_Lira  Station_Masindi  Station_Mbarara  Station_Mubende  \\\n",
      "0         False            False            False            False   \n",
      "1          True            False            False            False   \n",
      "2         False            False            False            False   \n",
      "3         False            False            False            False   \n",
      "4         False            False            False            False   \n",
      "\n",
      "   Station_Soroti  Station_Tororo  Station_Wadelai  \n",
      "0           False           False            False  \n",
      "1           False           False            False  \n",
      "2           False           False            False  \n",
      "3           False           False            False  \n",
      "4           False           False            False  \n"
     ]
    }
   ],
   "source": [
    "# Perform one-hot encoding on the 'Station' column in X\n",
    "X_encoded = pd.get_dummies(X, columns=['Station'], prefix='Station')\n",
    "\n",
    "\n",
    "# Remove extra spaces from column names in X_encoded\n",
    "X_encoded.columns = X_encoded.columns.str.strip()\n",
    "\n",
    "# Display the updated column names\n",
    "print(X_encoded.columns)\n",
    "\n",
    "\n",
    "# Display the encoded feature set\n",
    "print(\"\\nEncoded Feature Set (X):\")\n",
    "print(X_encoded.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2475bb3c",
   "metadata": {},
   "source": [
    "## Model training and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f4c5982",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ada3cf02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle the dataset\n",
    "X_encoded_shuffled, y_shuffled = shuffle(X_encoded, y_standardized, random_state=42)\n",
    "\n",
    "# Split the shuffled dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded_shuffled, y_shuffled, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b07a112",
   "metadata": {},
   "source": [
    "## Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b72680c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation RMSE Scores: [0.8658936  0.84390333 0.9313715  1.09687667 0.96951324]\n",
      "Mean Cross-Validation RMSE: 0.9415116665133002\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Define the linear regression model\n",
    "linear_reg_model = LinearRegression()\n",
    "\n",
    "#fit\n",
    "linear_reg_model.fit(X_train, y_train)\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "cv_scores = cross_val_score(linear_reg_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "cv_rmse_scores = np.sqrt(-cv_scores)\n",
    "print(\"Cross-Validation RMSE Scores:\", cv_rmse_scores)\n",
    "print(\"Mean Cross-Validation RMSE:\", cv_rmse_scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11592697",
   "metadata": {},
   "source": [
    "## RidgeCV model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b825a8ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression Cross-Validation RMSE Scores: [0.85790283 0.80087526 0.87736771 1.08864118 0.90667097]\n",
      "Mean Ridge Regression Cross-Validation RMSE: 0.9062915916415628\n",
      "Best Alpha value: 3.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "# Define the Ridge regression model with built-in cross-validation\n",
    "ridge_cv_model = RidgeCV(alphas=[0.1, 1.0, 2.0, 3.0, 5.0, 6.0, 7.0, 9.0, 10.0], cv=5)\n",
    "\n",
    "# Train the model on the training data\n",
    "ridge_cv_model.fit(X_train, y_train,)\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "cv_scores_ridge = cross_val_score(ridge_cv_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "cv_rmse_scores_ridge = np.sqrt(-cv_scores_ridge)\n",
    "print(\"Ridge Regression Cross-Validation RMSE Scores:\", cv_rmse_scores_ridge)\n",
    "print(\"Mean Ridge Regression Cross-Validation RMSE:\", cv_rmse_scores_ridge.mean())\n",
    "\n",
    "print(\"Best Alpha value:\", ridge_cv_model.alpha_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c754ee55",
   "metadata": {},
   "source": [
    "## DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7afe680b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Trees Cross-Validation RMSE Scores: [0.85555257 0.84390333 0.9313715  1.09687667 0.96951324]\n",
      "Mean Decision Trees Cross-Validation RMSE: 0.9394434607925284\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Define the Decision Trees model\n",
    "decision_tree_model = DecisionTreeRegressor(random_state=42)\n",
    "\n",
    "#fit\n",
    "decision_tree_model.fit(X_train, y_train)\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "decision_tree_cv_scores = cross_val_score(decision_tree_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "decision_tree_cv_rmse_scores = np.sqrt(-decision_tree_cv_scores)\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"Decision Trees Cross-Validation RMSE Scores:\", decision_tree_cv_rmse_scores)\n",
    "print(\"Mean Decision Trees Cross-Validation RMSE:\", decision_tree_cv_rmse_scores.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a87e783",
   "metadata": {},
   "source": [
    "## RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7dba6ad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Cross-Validation RMSE Scores: [0.85429178 0.83926772 0.93109006 1.09465439 0.95861835]\n",
      "Mean Random Forest Cross-Validation RMSE: 0.9355844605596115\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Define the Random Forest model\n",
    "random_forest_model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "random_forest_model.fit(X_train, y_train,)\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "random_forest_cv_scores = cross_val_score(random_forest_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "random_forest_cv_rmse_scores = np.sqrt(-random_forest_cv_scores)\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"Random Forest Cross-Validation RMSE Scores:\", random_forest_cv_rmse_scores)\n",
    "print(\"Mean Random Forest Cross-Validation RMSE:\", random_forest_cv_rmse_scores.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33415608",
   "metadata": {},
   "source": [
    "## Multi-Layer-Perceptron Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3796a168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN Cross-Validation RMSE Scores: [0.84652781 0.83201524 0.91507821 1.07923566 0.95762473]\n",
      "Mean ANN Cross-Validation RMSE: 0.9260963298581675\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "# Define the MLPRegressor model\n",
    "mlp_regressor_model = MLPRegressor(random_state=42)\n",
    "\n",
    "#fit\n",
    "mlp_regressor_model.fit(X_train, y_train)\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "mlp_cv_scores = cross_val_score(mlp_regressor_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "mlp_cv_rmse_scores = np.sqrt(-mlp_cv_scores)\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"ANN Cross-Validation RMSE Scores:\", mlp_cv_rmse_scores)\n",
    "print(\"Mean ANN Cross-Validation RMSE:\", mlp_cv_rmse_scores.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e1715f",
   "metadata": {},
   "source": [
    "## KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cdd87461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Cross-Validation RMSE Scores: [0.86223166 0.84477247 0.95403382 1.10348119 0.94585265]\n",
      "Mean KNN Cross-Validation RMSE: 0.9420743577505574\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Define the KNN model\n",
    "knn_model = KNeighborsRegressor()\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "knn_cv_scores = cross_val_score(knn_model,X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "knn_cv_rmse_scores = np.sqrt(-knn_cv_scores)\n",
    "\n",
    "# Print cross-validation results\n",
    "print(\"KNN Cross-Validation RMSE Scores:\", knn_cv_rmse_scores)\n",
    "print(\"Mean KNN Cross-Validation RMSE:\", knn_cv_rmse_scores.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9fb3e0",
   "metadata": {},
   "source": [
    "## Analyzing these results,\n",
    "- we can see that the Ridge Regression model has the lowest mean cross-validation RMSE of 63.49, followed closely by the Decision Tree and Random Forest models. These models generally outperform Linear Regression and KNN.\n",
    "- However, the Artificial Neural Network (MLPRegressor) has a significantly higher RMSE compared to other models, indicating poorer performance in this particular task.\n",
    "\n",
    "- Therefore, based on these results, the Ridge Regression model with an alpha value of 1.0 seems to be the best-performing model for the rainfall prediction task among the ones tested. \n",
    "- It offers a good balance between model complexity (regularization) and predictive accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351f147d",
   "metadata": {},
   "source": [
    "## Model selection\n",
    "- Based on the analysis of the cross-validation RMSE scores, the Ridge Regression model with an alpha value of 1.0 appears to be the best-performing model for the rainfall prediction task among the ones tested. \n",
    "- Therefore, we have selected the Ridge Regression model as our final choice for predicting rainfall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156ae5cd",
   "metadata": {},
   "source": [
    "## Define the Final Ridge regression model with built-in cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c269167f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression Cross-Validation RMSE Scores: [1.09577105 0.82711863 0.84500301 0.89853547 0.87057712]\n",
      "Mean Ridge Regression Cross-Validation RMSE: 0.9074010586701491\n"
     ]
    }
   ],
   "source": [
    "\n",
    "ridge_cv_modelfinal = RidgeCV(alphas= [3.0], cv=5)  # Setting alpha value to 3.0\n",
    "\n",
    "# Train the model on the entire dataset\n",
    "ridge_cv_modelfinal.fit(X_encoded_shuffled, y_shuffled)\n",
    "\n",
    "\n",
    "# Perform k-fold cross-validation\n",
    "cv_scores_ridge = cross_val_score(ridge_cv_modelfinal, X_encoded_shuffled, y_shuffled, cv=5, scoring='neg_mean_squared_error')\n",
    "cv_rmse_scores_ridge = np.sqrt(-cv_scores_ridge)\n",
    "print(\"Ridge Regression Cross-Validation RMSE Scores:\", cv_rmse_scores_ridge)\n",
    "print(\"Mean Ridge Regression Cross-Validation RMSE:\", cv_rmse_scores_ridge.mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc1bf19",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "88ee3a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def predict_rainfall(station, month, model, scaler, encoded_columns):\n",
    "    # Strip any leading/trailing spaces from station name\n",
    "    station = station.strip()\n",
    "    \n",
    "    # Create a feature vector with one-hot encoded station\n",
    "    feature_vector = np.zeros(len(encoded_columns))\n",
    "    feature_vector[encoded_columns.get_loc(f'Station_{station}')] = 1\n",
    "    \n",
    "    # Predict the rainfall using the model\n",
    "    rainfall_prediction_standardized = model.predict([feature_vector])\n",
    "    \n",
    "    # Inverse transform the predicted rainfall to the original scale\n",
    "    rainfall_prediction_original_scale = scaler.inverse_transform(rainfall_prediction_standardized)\n",
    "\n",
    "    \n",
    "    # Return the predicted rainfall value for the specific month\n",
    "    print(f'{model}->{station}->{month}: {rainfall_prediction_original_scale[0][encode_month(month)]} mm')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3680fd58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_month(month):\n",
    "    # Define a dictionary to map month names to numerical values\n",
    "    month_mapping = {\n",
    "        'Jan': 0, 'Feb': 1, 'Mar': 2, 'Apr': 3, 'May': 4, 'Jun': 5,\n",
    "        'Jul': 6, 'Aug': 7, 'Sep': 8, 'Oct': 9, 'Nov': 10, 'Dec': 11\n",
    "    }\n",
    "    \n",
    "    # Return the numerical value corresponding to the month\n",
    "    return month_mapping.get(month, 0)  # Return 0 if month not found in dictionary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b8426f6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeCV(alphas=[3.0], cv=5)->Kitgum->Apr: 182.2005939849624 mm\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JOSEPH\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but RidgeCV was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# predicting rainfall forspecific months\n",
    "predict_rainfall(\"Kitgum\", \"Apr\", ridge_cv_modelfinal, scaler, X_encoded.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49a4b8d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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