RainDetection.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9bc9ba0a-e384-4bed-adee-187c10a0f354",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "614336de-3938-4e8a-90d0-186b591531d8",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>MaxT</th>\n",
       "      <th>MinT</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Precipitation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2006-01-08 00:00:00</td>\n",
       "      <td>29.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>70.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-09 00:00:00</td>\n",
       "      <td>30.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>67.4</td>\n",
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       "      <td>2006-01-10 00:00:00</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-11 00:00:00</td>\n",
       "      <td>29.8</td>\n",
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       "      <th>4</th>\n",
       "      <td>2006-01-12 00:00:00</td>\n",
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       "      <th>6233</th>\n",
       "      <td>2023-02-01 00:00:00</td>\n",
       "      <td>34.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6234</th>\n",
       "      <td>2023-02-02 00:00:00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>2023-02-03 00:00:00</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "<p>6236 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "                     Date  MaxT  MinT  WindSpeed  Humidity   Precipitation\n",
       "0     2006-01-08 00:00:00  29.5  21.8        1.6       70.2            0.0\n",
       "1     2006-01-09 00:00:00  30.1  21.0        1.6       67.4            0.0\n",
       "2     2006-01-10 00:00:00  29.9  21.1        1.7       65.2            0.0\n",
       "3     2006-01-11 00:00:00  29.8  20.8        1.5       69.0            0.0\n",
       "4     2006-01-12 00:00:00  29.6  20.5        2.1       69.5            0.0\n",
       "...                   ...   ...   ...        ...        ...            ...\n",
       "6231  2023-01-30 00:00:00  34.0  19.0        4.0       63.0            0.0\n",
       "6232  2023-01-31 00:00:00  37.0  19.0        2.9       57.5            0.0\n",
       "6233  2023-02-01 00:00:00  34.0  20.0        2.3       55.0            0.0\n",
       "6234  2023-02-02 00:00:00  38.0  19.0        2.4       54.0            0.0\n",
       "6235  2023-02-03 00:00:00  37.0  19.0        2.3       57.0            0.0\n",
       "\n",
       "[6236 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Farm_Weather_Data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8703d3a6-c1e7-4cfe-9c5e-66b981feeca7",
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    {
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       "      <td>0.0</td>\n",
       "      <td>2023</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "      MaxT  MinT  WindSpeed  Humidity   Precipitation  Year  Month  Day\n",
       "0     29.5  21.8        1.6       70.2            0.0  2006      1    8\n",
       "1     30.1  21.0        1.6       67.4            0.0  2006      1    9\n",
       "2     29.9  21.1        1.7       65.2            0.0  2006      1   10\n",
       "3     29.8  20.8        1.5       69.0            0.0  2006      1   11\n",
       "4     29.6  20.5        2.1       69.5            0.0  2006      1   12\n",
       "...    ...   ...        ...        ...            ...   ...    ...  ...\n",
       "6231  34.0  19.0        4.0       63.0            0.0  2023      1   30\n",
       "6232  37.0  19.0        2.9       57.5            0.0  2023      1   31\n",
       "6233  34.0  20.0        2.3       55.0            0.0  2023      2    1\n",
       "6234  38.0  19.0        2.4       54.0            0.0  2023      2    2\n",
       "6235  37.0  19.0        2.3       57.0            0.0  2023      2    3\n",
       "\n",
       "[6236 rows x 8 columns]"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    " # Clean data\n",
    "df = df[df['MaxT'] >= df['MinT']]   # Remove rows where MaxT is less than MinT\n",
    "df['Date'] = df['Date'].str.split().str[0]   # Extract date part of Date column\n",
    "#df = df[(df['Humidity'] >= 60) & (df['Precipitation'] > 0.5)]\n",
    "\n",
    "# extra columns for analysis\n",
    "df['Year'] = df['Date'].apply(lambda x: int(x.split('-')[0]))\n",
    "df['Month'] = df['Date'].apply(lambda x: int(x.split('-')[1]))\n",
    "df['Day'] = df['Date'].apply(lambda x: int(x.split('-')[2]))\n",
    "df.drop(['Date'], axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "404932ff-29d1-4ec0-80ef-54d02936d627",
   "metadata": {},
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    {
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       "      <th>Year</th>\n",
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       "      <td>2006</td>\n",
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       "      <td>2023</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>57.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6233</th>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>55.0</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>54.0</td>\n",
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       "      <td>19.0</td>\n",
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      ],
      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation\n",
       "0     2006      1    8  29.5  21.8        1.6       70.2            0.0\n",
       "1     2006      1    9  30.1  21.0        1.6       67.4            0.0\n",
       "2     2006      1   10  29.9  21.1        1.7       65.2            0.0\n",
       "3     2006      1   11  29.8  20.8        1.5       69.0            0.0\n",
       "4     2006      1   12  29.6  20.5        2.1       69.5            0.0\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...\n",
       "6231  2023      1   30  34.0  19.0        4.0       63.0            0.0\n",
       "6232  2023      1   31  37.0  19.0        2.9       57.5            0.0\n",
       "6233  2023      2    1  34.0  20.0        2.3       55.0            0.0\n",
       "6234  2023      2    2  38.0  19.0        2.4       54.0            0.0\n",
       "6235  2023      2    3  37.0  19.0        2.3       57.0            0.0\n",
       "\n",
       "[6236 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_order = ['Year', 'Month', 'Day'] + [col for col in df.columns if col not in ['Year', 'Month', 'Day']]\n",
    "\n",
    "# Reindex the DataFrame using the new column order\n",
    "df = df[column_order]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "776ff723-768d-43a5-bd6a-0b1426f6e4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year             0\n",
       "Month            0\n",
       "Day              0\n",
       "MaxT             0\n",
       "MinT             0\n",
       "WindSpeed        0\n",
       "Humidity         0\n",
       "Precipitation    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbae78f0-ebe5-4879-ab7b-b9c440252d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>MaxT</th>\n",
       "      <th>MinT</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>70.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>67.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>29.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>65.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>29.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>29.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6197</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>64.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6202 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation\n",
       "0     2006      1    8  29.5  21.8        1.6       70.2            0.0\n",
       "1     2006      1    9  30.1  21.0        1.6       67.4            0.0\n",
       "2     2006      1   10  29.9  21.1        1.7       65.2            0.0\n",
       "3     2006      1   11  29.8  20.8        1.5       69.0            0.0\n",
       "4     2006      1   12  29.6  20.5        2.1       69.5            0.0\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...\n",
       "6197  2022     12   27  34.0  21.0        2.0       72.0            0.0\n",
       "6198  2022     12   28  35.0  21.0        1.6       68.0            0.0\n",
       "6199  2022     12   29  35.0  22.0        1.8       67.0            0.0\n",
       "6200  2022     12   30  35.0  21.0        1.2       64.5            0.0\n",
       "6201  2022     12   31  34.0  21.0        1.6       68.0            0.0\n",
       "\n",
       "[6202 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Year'] < 2023]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a5120b3-a3cc-4133-bf9c-2e6456ec9534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    6202.000000\n",
      "mean        3.445292\n",
      "std         8.288860\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         2.600000\n",
      "max       128.300000\n",
      "Name: Precipitation, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a statistical summary\n",
    "print(df['Precipitation'].describe())\n",
    "\n",
    "# Plot the distribution\n",
    "import matplotlib.pyplot as plt\n",
    "df['Precipitation'].hist(bins=50)\n",
    "plt.title('Precipitation Distribution')\n",
    "plt.xlabel('Precipitation (mm)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09624ecb-ead9-4480-ba8d-8bcf3a480cbc",
   "metadata": {},
   "source": [
    "# Creating different rain categories\n",
    "0 = No rain <br>\n",
    "1 = Light rain <br>\n",
    "2 = Moderate rain <br>\n",
    "3 = Heavy rain <br>\n",
    "4 = Extreme rain <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d8f165e-b185-44fa-b737-799fbb45053e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/r3/x7gh_t194j50jzy8hrs8_2xw0000gn/T/ipykernel_40231/1198773640.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df.loc[:, 'Rain'] = df['Precipitation'].apply(classify_rainfall)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>MaxT</th>\n",
       "      <th>MinT</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>70.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>67.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>29.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>65.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>29.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>29.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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>6197</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>64.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>6202 rows × 9 columns</p>\n",
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      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation  Rain\n",
       "0     2006      1    8  29.5  21.8        1.6       70.2            0.0     0\n",
       "1     2006      1    9  30.1  21.0        1.6       67.4            0.0     0\n",
       "2     2006      1   10  29.9  21.1        1.7       65.2            0.0     0\n",
       "3     2006      1   11  29.8  20.8        1.5       69.0            0.0     0\n",
       "4     2006      1   12  29.6  20.5        2.1       69.5            0.0     0\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...   ...\n",
       "6197  2022     12   27  34.0  21.0        2.0       72.0            0.0     0\n",
       "6198  2022     12   28  35.0  21.0        1.6       68.0            0.0     0\n",
       "6199  2022     12   29  35.0  22.0        1.8       67.0            0.0     0\n",
       "6200  2022     12   30  35.0  21.0        1.2       64.5            0.0     0\n",
       "6201  2022     12   31  34.0  21.0        1.6       68.0            0.0     0\n",
       "\n",
       "[6202 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def classify_rainfall(amount):\n",
    "    if amount == 0:\n",
    "        return 0  # No rain\n",
    "    elif amount < 2.5:\n",
    "        return 1  # Light rain\n",
    "    elif amount <= 7.6:  # Adjusted to include up to 7.6 as moderate\n",
    "        return 2  # Moderate rain\n",
    "    elif amount <= 50:  # Adjusted to include up to 50 as heavy\n",
    "        return 3  # Heavy rain\n",
    "    else:\n",
    "        return 4  # Extreme rain\n",
    "\n",
    "# Apply the classification function to the 'Precipitation' column\n",
    "df.loc[:, 'Rain'] = df['Precipitation'].apply(classify_rainfall)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7015969-8ae9-4d11-9f93-0b48d8fc73cd",
   "metadata": {},
   "source": [
    "## No Rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "79823262-baab-4a0c-aba2-d17a5a6e172a",
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   "outputs": [
    {
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       "      <td>20.8</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
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       "      <td>12</td>\n",
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       "      <td>20.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>69.5</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>12</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
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       "      <td>64.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6201</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "<p>4245 rows × 9 columns</p>\n",
       "</div>"
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      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation  Rain\n",
       "0     2006      1    8  29.5  21.8        1.6       70.2            0.0     0\n",
       "1     2006      1    9  30.1  21.0        1.6       67.4            0.0     0\n",
       "2     2006      1   10  29.9  21.1        1.7       65.2            0.0     0\n",
       "3     2006      1   11  29.8  20.8        1.5       69.0            0.0     0\n",
       "4     2006      1   12  29.6  20.5        2.1       69.5            0.0     0\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...   ...\n",
       "6197  2022     12   27  34.0  21.0        2.0       72.0            0.0     0\n",
       "6198  2022     12   28  35.0  21.0        1.6       68.0            0.0     0\n",
       "6199  2022     12   29  35.0  22.0        1.8       67.0            0.0     0\n",
       "6200  2022     12   30  35.0  21.0        1.2       64.5            0.0     0\n",
       "6201  2022     12   31  34.0  21.0        1.6       68.0            0.0     0\n",
       "\n",
       "[4245 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Rain'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760d73f3-abab-4479-9e27-feac7502f853",
   "metadata": {},
   "source": [
    "## Light Rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3af0d3e6-20ef-4d90-bce4-c4acb39ada1a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>502</th>\n",
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       "      <td>80.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6144</th>\n",
       "      <td>2022</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
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       "      <td>77.0</td>\n",
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       "    <tr>\n",
       "      <th>6165</th>\n",
       "      <td>2022</td>\n",
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       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>6182</th>\n",
       "      <td>2022</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
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       "<p>383 rows × 9 columns</p>\n",
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      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation  Rain\n",
       "454   2007      4    7  34.5  25.5        2.6       58.0            2.1     1\n",
       "479   2007      5    2  36.6  27.9        2.3       57.4            2.2     1\n",
       "480   2007      5    3  35.4  26.0        2.5       61.9            2.2     1\n",
       "502   2007      5   25  38.8  28.8        3.3       51.4            2.3     1\n",
       "664   2007     11    3  31.9  24.5        1.4       79.4            1.6     1\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...   ...\n",
       "6143  2022     11    3  30.0  24.0        1.9       80.0            1.1     1\n",
       "6144  2022     11    4  31.0  23.0        2.6       77.0            1.7     1\n",
       "6165  2022     11   25  31.0  23.0        2.2       75.0            1.1     1\n",
       "6182  2022     12   12  30.0  22.0        3.5       77.0            0.2     1\n",
       "6183  2022     12   13  32.0  22.0        2.9       75.5            0.4     1\n",
       "\n",
       "[383 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Rain'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373b73aa-1128-425b-8879-bd7ec3291798",
   "metadata": {},
   "source": [
    "## Moderate Rain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b61f281d-565b-4306-ba8c-ef3f69086c5b",
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    {
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>35.4</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>37.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>52.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6119</th>\n",
       "      <td>2022</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6120</th>\n",
       "      <td>2022</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>2022</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>31.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.6</td>\n",
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       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>2022</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6145</th>\n",
       "      <td>2022</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation  Rain\n",
       "54    2006      3    3  32.1  24.3        2.8       71.3            4.8     2\n",
       "56    2006      3    5  32.9  24.6        2.3       69.6            4.8     2\n",
       "113   2006      5    1  37.4  28.1        3.2       59.9            4.7     2\n",
       "128   2006      5   16  35.4  29.0        3.1       50.0            7.3     2\n",
       "131   2006      5   19  37.4  27.0        3.3       52.1            4.9     2\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...   ...\n",
       "6119  2022     10   10  27.0  23.0        4.1       91.0            3.3     2\n",
       "6120  2022     10   11  30.0  24.0        2.7       86.0            2.8     2\n",
       "6127  2022     10   18  31.0  24.0        1.3       82.0            2.6     2\n",
       "6130  2022     10   21  30.0  23.0        3.5       82.5            2.6     2\n",
       "6145  2022     11    5  29.0  23.0        3.0       81.0            2.8     2\n",
       "\n",
       "[622 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df['Rain'] == 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b97f9e-9bc6-4eb3-9376-eb4056548767",
   "metadata": {},
   "source": [
    "## Heavy Rain"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4c1f24c-8921-4bc2-be63-03eba4c47ead",
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       "      <th>132</th>\n",
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       "      <th>6088</th>\n",
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       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation  Rain\n",
       "98    2006      4   16  34.5  26.9        3.0       76.0            9.8     3\n",
       "99    2006      4   17  28.3  22.9        2.4       72.4            9.8     3\n",
       "125   2006      5   13  37.8  29.6        3.3       61.5            8.4     3\n",
       "129   2006      5   17  35.8  25.4        3.3       54.8           14.7     3\n",
       "132   2006      5   20  31.3  25.4        3.9       56.5           14.7     3\n",
       "...    ...    ...  ...   ...   ...        ...        ...            ...   ...\n",
       "6062  2022      8   14  24.0  24.0        7.9       94.0           26.8     3\n",
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       "6089  2022      9   10  28.0  24.0        4.3       91.5           11.1     3\n",
       "6107  2022      9   28  33.0  25.0        1.5       79.0            9.2     3\n",
       "6114  2022     10    5  29.0  25.0        3.0       89.0            7.7     3\n",
       "\n",
       "[926 rows x 9 columns]"
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     "execution_count": 12,
     "metadata": {},
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    "df[df['Rain'] == 3]"
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   "cell_type": "markdown",
   "id": "c769c75c-3ee4-4e5d-992d-be0ed66e1795",
   "metadata": {},
   "source": [
    "## Extreme Rain"
   ]
  },
  {
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   "execution_count": 13,
   "id": "52b7f79d-57ff-4b57-8b17-3aa53daf3b8c",
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   "outputs": [
    {
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       "      <td>29.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>75.3</td>\n",
       "      <td>54.7</td>\n",
       "      <td>4</td>\n",
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       "      <td>29.3</td>\n",
       "      <td>25.6</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>2848</th>\n",
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       "      <td>87.9</td>\n",
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       "      <td>24.2</td>\n",
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       "      <td>73.6</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>83.1</td>\n",
       "      <td>56.6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4959</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>28.6</td>\n",
       "      <td>24.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>88.1</td>\n",
       "      <td>57.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5221</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>36.6</td>\n",
       "      <td>25.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>49.9</td>\n",
       "      <td>51.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5266</th>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>36.3</td>\n",
       "      <td>27.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.2</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5362</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>30.2</td>\n",
       "      <td>25.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>84.6</td>\n",
       "      <td>77.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>28.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>87.8</td>\n",
       "      <td>110.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5391</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>85.8</td>\n",
       "      <td>128.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5392</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>29.8</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>86.8</td>\n",
       "      <td>67.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5633</th>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>86.0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5635</th>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>90.5</td>\n",
       "      <td>92.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5652</th>\n",
       "      <td>2021</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>35.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>71.5</td>\n",
       "      <td>53.8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Month  Day  MaxT  MinT  WindSpeed  Humidity   Precipitation  Rain\n",
       "206   2006      8    2  27.4  25.7        4.6       69.1           51.2     4\n",
       "207   2006      8    3  25.7  24.5        4.7       70.6           51.2     4\n",
       "208   2006      8    4  27.0  24.4        5.1       69.0           51.2     4\n",
       "294   2006     10   29  24.9  23.4        2.2       80.6           57.4     4\n",
       "295   2006     10   30  26.2  23.5        2.2       81.8           85.1     4\n",
       "1756  2010     10   30  26.3  23.6        2.6       88.9           50.2     4\n",
       "2426  2012      8   30  29.2  24.9        3.4       75.3           54.7     4\n",
       "2740  2013      7   10  29.3  25.6        5.0       77.8           50.3     4\n",
       "2844  2013     10   22  26.4  24.9        2.8       91.1           66.2     4\n",
       "2848  2013     10   26  28.8  25.3        3.1       87.9           86.2     4\n",
       "3043  2014      5    9  32.1  26.6        3.1       69.7           51.6     4\n",
       "3199  2014     10   12  28.3  25.5        4.0       52.5           62.4     4\n",
       "3449  2015      6   19  27.4  24.7        5.2       78.0           56.2     4\n",
       "3450  2015      6   20  27.6  24.2        5.7       73.6           56.2     4\n",
       "4606  2018      8   19  28.1  25.4        4.2       81.9           55.1     4\n",
       "4958  2019      8    6  28.4  25.5        5.2       83.1           56.6     4\n",
       "4959  2019      8    7  28.6  24.6        5.4       88.1           57.2     4\n",
       "5221  2020      4   25  36.6  25.9        3.7       49.9           51.2     4\n",
       "5266  2020      6    9  36.3  27.1        3.0       59.2           56.0     4\n",
       "5362  2020      9   13  30.2  25.3        2.9       84.6           77.9     4\n",
       "5390  2020     10   11  28.4  25.0        2.0       87.8          110.5     4\n",
       "5391  2020     10   12  28.0  25.0        1.8       85.8          128.3     4\n",
       "5392  2020     10   13  29.8  25.4        3.6       86.8           67.5     4\n",
       "5633  2021      6   11  28.0  24.0        6.3       86.0           55.9     4\n",
       "5635  2021      6   13  25.0  22.0        4.7       90.5           92.8     4\n",
       "5652  2021      6   30  35.0  27.0        3.3       71.5           53.8     4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Rain'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11830b0d-ed1e-46ff-b1cd-54b1d026ff37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A correlation heat map for each pair of explanatory variables.\n",
    "plt.figure(figsize=(10, 5))\n",
    "corr_matrix = df.corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b09f20-bdc5-4b4b-88fc-6898e3bce78f",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c77808e-3350-433b-94c8-ac73c09bcad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assume 'df' is your DataFrame and 'Rain' is the target variable\n",
    "X = df.drop(['Precipitation', 'Rain'], axis=1)  # Drop the target and original Precipitation column\n",
    "y = df['Rain']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "feb3bb0d-063b-42bf-8506-a428e0661519",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150090bf-0b20-481a-adff-deeb9d600c6f",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52211b50-cfc1-45e1-872d-8074ba79c049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500, multi_class=&#x27;multinomial&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=500, multi_class=&#x27;multinomial&#x27;, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=500, multi_class='multinomial', random_state=42)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Initialize the logistic regression model for multiclass classification\n",
    "# 'multinomial' specifies that you want softmax regression\n",
    "# 'lbfgs' solver supports multinomial logistic regression\n",
    "model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=500, random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c354c22-3853-441a-b2a4-b249ba84ec83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.750201450443191\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      No Rain       0.80      0.94      0.86       881\n",
      "   Light Rain       0.55      0.32      0.40        73\n",
      "Moderate Rain       0.33      0.01      0.02       113\n",
      "   Heavy Rain       0.49      0.46      0.47       170\n",
      " Extreme Rain       0.00      0.00      0.00         4\n",
      "\n",
      "     accuracy                           0.75      1241\n",
      "    macro avg       0.43      0.34      0.35      1241\n",
      " weighted avg       0.70      0.75      0.70      1241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Predict on the testing set\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "\n",
    "# Detailed classification report\n",
    "report = classification_report(y_test, y_pred, target_names=['No Rain', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Extreme Rain'], zero_division=0)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4b1b94-1be4-4050-bee4-b320cddb4c05",
   "metadata": {},
   "source": [
    "## Random Forrest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7481ffb2-dfbb-4f29-b0f3-7dc88feabc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2e61bc8-e4df-43ba-8938-f8c8992321e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Random Forest classifier\n",
    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "rf_classifier.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "585374f6-6d27-4894-911a-64d7a394826a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7751813053988719\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      No Rain       0.84      0.95      0.89       881\n",
      "   Light Rain       0.51      0.36      0.42        73\n",
      "Moderate Rain       0.30      0.12      0.17       113\n",
      "   Heavy Rain       0.58      0.52      0.55       170\n",
      " Extreme Rain       0.00      0.00      0.00         4\n",
      "\n",
      "     accuracy                           0.78      1241\n",
      "    macro avg       0.45      0.39      0.41      1241\n",
      " weighted avg       0.73      0.78      0.75      1241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the test data\n",
    "y_pred = rf_classifier.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "\n",
    "# Print a classification report to see precision, recall, and F1-score for each class\n",
    "print(classification_report(y_test, y_pred, target_names=['No Rain', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Extreme Rain']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349870d7-149c-4ba8-afc9-cbd03d1b087b",
   "metadata": {},
   "source": [
    "## Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "611b8002-0408-4814-bdde-02ef3336a71d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with Gradient Boosting Classifier: 0.7840451248992748\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      No Rain       0.84      0.95      0.89       881\n",
      "   Light Rain       0.52      0.58      0.55        73\n",
      "Moderate Rain       0.44      0.07      0.12       113\n",
      "   Heavy Rain       0.61      0.51      0.56       170\n",
      " Extreme Rain       0.20      0.25      0.22         4\n",
      "\n",
      "     accuracy                           0.78      1241\n",
      "    macro avg       0.52      0.47      0.47      1241\n",
      " weighted avg       0.75      0.78      0.75      1241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Initialize the Gradient Boosting Classifier\n",
    "gbm = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "gbm.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict on the testing set\n",
    "y_pred_gbm = gbm.predict(X_test_scaled)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy_gbm = accuracy_score(y_test, y_pred_gbm)\n",
    "print(f\"Accuracy with Gradient Boosting Classifier: {accuracy_gbm}\")\n",
    "\n",
    "# Detailed classification report\n",
    "report_gbm = classification_report(y_test, y_pred_gbm, target_names=['No Rain', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Extreme Rain'])\n",
    "print(report_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c441bf97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gradient_boost_model.joblib']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "# Assuming 'cat_model' is your trained CatBoostClassifier\n",
    "dump(gbm, 'gradient_boost_model.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bf9411-34ee-483c-a59a-d3b57e22a337",
   "metadata": {},
   "source": [
    "## XGBoost and CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ec47ead-6d69-49b8-850c-ac630a76e156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with Gradient Boosting Classifier: 0.7759871071716358\n",
      "XGBoost Performance:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      No Rain       0.86      0.93      0.89       881\n",
      "   Light Rain       0.52      0.44      0.48        73\n",
      "Moderate Rain       0.29      0.15      0.20       113\n",
      "   Heavy Rain       0.57      0.55      0.56       170\n",
      " Extreme Rain       0.00      0.00      0.00         4\n",
      "\n",
      "     accuracy                           0.78      1241\n",
      "    macro avg       0.45      0.41      0.43      1241\n",
      " weighted avg       0.74      0.78      0.76      1241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "# Initialize the XGBClassifier\n",
    "xgb_model = XGBClassifier(objective='multi:softmax', num_class=5, use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# Fit the model\n",
    "xgb_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict\n",
    "y_pred_xgb = xgb_model.predict(X_test_scaled)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy_xgb = accuracy_score(y_test, y_pred_xgb)\n",
    "print(f\"Accuracy with Gradient Boosting Classifier: {accuracy_xgb}\")\n",
    "\n",
    "# Evaluate\n",
    "print(\"XGBoost Performance:\")\n",
    "print(classification_report(y_test, y_pred_xgb, target_names=['No Rain', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Extreme Rain']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e70b18d0-079b-41ee-adb3-dc49a1975e5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with Cat Boost Classifier: 0.7856567284448026\n",
      "CatBoost Performance:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      No Rain       0.86      0.94      0.90       881\n",
      "   Light Rain       0.54      0.41      0.47        73\n",
      "Moderate Rain       0.29      0.14      0.19       113\n",
      "   Heavy Rain       0.61      0.58      0.59       170\n",
      " Extreme Rain       0.67      0.50      0.57         4\n",
      "\n",
      "     accuracy                           0.79      1241\n",
      "    macro avg       0.59      0.51      0.54      1241\n",
      " weighted avg       0.75      0.79      0.77      1241\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "# Initialize the CatBoostClassifier\n",
    "cat_model = CatBoostClassifier(verbose=0)  # 'verbose=0' to silence the output\n",
    "\n",
    "# Fit the model\n",
    "cat_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict\n",
    "y_pred_cat = cat_model.predict(X_test_scaled)\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy_cat = accuracy_score(y_test, y_pred_cat)\n",
    "print(f\"Accuracy with Cat Boost Classifier: {accuracy_cat}\")\n",
    "\n",
    "# Evaluate\n",
    "print(\"CatBoost Performance:\")\n",
    "print(classification_report(y_test, y_pred_cat, target_names=['No Rain', 'Light Rain', 'Moderate Rain', 'Heavy Rain', 'Extreme Rain']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e4096c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for each fold: [0.73011078 0.75201613 0.75100806 0.74495968 0.7358871 ]\n",
      "Mean CV Accuracy: 0.7427963486339864\n",
      "Standard Deviation of CV Accuracy: 0.008553106436629751\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "import numpy as np\n",
    "\n",
    "# Initialize the CatBoostClassifier\n",
    "cat_model = CatBoostClassifier(verbose=0)  # 'verbose=0' to silence the output\n",
    "\n",
    "# Define Stratified K-Folds cross-validator\n",
    "# Stratified K-Folds is used to maintain the percentage of samples for each class\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Perform cross-validation\n",
    "# Note: Ensure that your X_train_scaled and y_train are correctly defined and scaled\n",
    "# X and y should be the entire dataset (not split) if you're evaluating the model's overall performance with CV\n",
    "scores = cross_val_score(cat_model, X_train_scaled, y_train, cv=cv, scoring='accuracy')\n",
    "\n",
    "# Print the accuracy for each fold\n",
    "print(f\"Accuracy for each fold: {scores}\")\n",
    "\n",
    "# Calculate and print the mean and standard deviation of the cross-validation scores\n",
    "print(f\"Mean CV Accuracy: {np.mean(scores)}\")\n",
    "print(f\"Standard Deviation of CV Accuracy: {np.std(scores)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e823db1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "# Assuming X and y are your features and target variable respectively\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Convert labels to categorical one-hot encoding\n",
    "y_train_categorical = to_categorical(y_train, num_classes=5)\n",
    "y_test_categorical = to_categorical(y_test, num_classes=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "632c3ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# Define the model\n",
    "model = Sequential([\n",
    "    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(5, activation='softmax')  # Output layer: 5 units for 5 classes\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f19e1415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "124/124 [==============================] - 0s 929us/step - loss: 0.9399 - accuracy: 0.6689 - val_loss: 0.7486 - val_accuracy: 0.7251\n",
      "Epoch 2/10\n",
      "124/124 [==============================] - 0s 508us/step - loss: 0.7196 - accuracy: 0.7331 - val_loss: 0.7008 - val_accuracy: 0.7362\n",
      "Epoch 3/10\n",
      "124/124 [==============================] - 0s 513us/step - loss: 0.6853 - accuracy: 0.7422 - val_loss: 0.6886 - val_accuracy: 0.7442\n",
      "Epoch 4/10\n",
      "124/124 [==============================] - 0s 770us/step - loss: 0.6681 - accuracy: 0.7480 - val_loss: 0.6792 - val_accuracy: 0.7432\n",
      "Epoch 5/10\n",
      "124/124 [==============================] - 0s 498us/step - loss: 0.6564 - accuracy: 0.7505 - val_loss: 0.6756 - val_accuracy: 0.7432\n",
      "Epoch 6/10\n",
      "124/124 [==============================] - 0s 496us/step - loss: 0.6498 - accuracy: 0.7573 - val_loss: 0.6703 - val_accuracy: 0.7452\n",
      "Epoch 7/10\n",
      "124/124 [==============================] - 0s 493us/step - loss: 0.6434 - accuracy: 0.7553 - val_loss: 0.6700 - val_accuracy: 0.7442\n",
      "Epoch 8/10\n",
      "124/124 [==============================] - 0s 512us/step - loss: 0.6414 - accuracy: 0.7566 - val_loss: 0.6672 - val_accuracy: 0.7503\n",
      "Epoch 9/10\n",
      "124/124 [==============================] - 0s 516us/step - loss: 0.6340 - accuracy: 0.7588 - val_loss: 0.6648 - val_accuracy: 0.7432\n",
      "Epoch 10/10\n",
      "124/124 [==============================] - 0s 497us/step - loss: 0.6323 - accuracy: 0.7588 - val_loss: 0.6702 - val_accuracy: 0.7472\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train_scaled, y_train_categorical,\n",
    "                    validation_split=0.2,\n",
    "                    epochs=10,\n",
    "                    batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b87c0d63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39/39 [==============================] - 0s 359us/step - loss: 0.6027 - accuracy: 0.7703\n",
      "Test accuracy: 0.7703465223312378\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test_scaled, y_test_categorical)\n",
    "print(f\"Test accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c78ec86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_artifacts.joblib']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "# Wrap model and scaler together in a dictionary or a custom class\n",
    "model_artifacts = {\n",
    "    'scaler': scaler,\n",
    "    'model': model\n",
    "}\n",
    "\n",
    "# Save the model artifacts\n",
    "dump(model_artifacts, 'model_artifacts.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e432ca89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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