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      "metadata": {
        "id": "3CHv5rrGiN2h"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import metrics\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Rainfall.csv')\n",
        "df.head()"
      ],
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            "text/plain": [
              "   day  pressure   maxtemp  temparature  mintemp  dewpoint  humidity   cloud   \\\n",
              "0    1     1025.9     19.9         18.3     16.8      13.1         72      49   \n",
              "1    2     1022.0     21.7         18.9     17.2      15.6         81      83   \n",
              "2    3     1019.7     20.3         19.3     18.0      18.4         95      91   \n",
              "3    4     1018.9     22.3         20.6     19.1      18.8         90      88   \n",
              "4    5     1015.9     21.3         20.7     20.2      19.9         95      81   \n",
              "\n",
              "  rainfall  sunshine           winddirection  windspeed  \n",
              "0      yes       9.3                    80.0       26.3  \n",
              "1      yes       0.6                    50.0       15.3  \n",
              "2      yes       0.0                    40.0       14.2  \n",
              "3      yes       1.0                    50.0       16.9  \n",
              "4      yes       0.0                    40.0       13.7  "
            ],
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              "      <th>day</th>\n",
              "      <th>pressure</th>\n",
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              "      <td>1</td>\n",
              "      <td>1025.9</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1019.7</td>\n",
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              "      <td>yes</td>\n",
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              "      <td>40.0</td>\n",
              "      <td>14.2</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1018.9</td>\n",
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              "      <td>19.1</td>\n",
              "      <td>18.8</td>\n",
              "      <td>90</td>\n",
              "      <td>88</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>16.9</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1015.9</td>\n",
              "      <td>21.3</td>\n",
              "      <td>20.7</td>\n",
              "      <td>20.2</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 366,\n  \"fields\": [\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          28,\n          16,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pressure \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.414776409895459,\n        \"min\": 998.5,\n        \"max\": 1034.6,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          1016.2,\n          1016.8,\n          1019.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"maxtemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.978342653428501,\n        \"min\": 7.1,\n        \"max\": 36.3,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          22.1,\n          34.7,\n          28.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temparature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.632812934538626,\n        \"min\": 4.9,\n        \"max\": 32.4,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          28.6,\n          20.8,\n          32.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mintemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.594152786625049,\n        \"min\": 3.1,\n        \"max\": 30.0,\n        \"num_unique_values\": 157,\n        \"samples\": [\n          24.9,\n          14.0,\n          21.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dewpoint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.997021243267362,\n        \"min\": -0.4,\n        \"max\": 26.7,\n        \"num_unique_values\": 158,\n        \"samples\": [\n          16.6,\n          13.3,\n          21.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 36,\n        \"max\": 98,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          61,\n          56,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cloud \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          76,\n          49,\n          94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sunshine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.9343977512568165,\n        \"min\": 0.0,\n        \"max\": 12.1,\n        \"num_unique_values\": 104,\n        \"samples\": [\n          0.5,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"         winddirection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 81.72372436011267,\n        \"min\": 10.0,\n        \"max\": 350.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          290.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.06971177781536,\n        \"min\": 4.4,\n        \"max\": 59.5,\n        \"num_unique_values\": 223,\n        \"samples\": [\n          37.7,\n          19.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 26
        }
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(366, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
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    {
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      "source": [
        "df.info()"
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        "id": "poTflnkziU0q",
        "outputId": "5eebae07-3c4e-4fe8-b266-7b1a27a4992b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 366 entries, 0 to 365\n",
            "Data columns (total 12 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   day                     366 non-null    int64  \n",
            " 1   pressure                366 non-null    float64\n",
            " 2   maxtemp                 366 non-null    float64\n",
            " 3   temparature             366 non-null    float64\n",
            " 4   mintemp                 366 non-null    float64\n",
            " 5   dewpoint                366 non-null    float64\n",
            " 6   humidity                366 non-null    int64  \n",
            " 7   cloud                   366 non-null    int64  \n",
            " 8   rainfall                366 non-null    object \n",
            " 9   sunshine                366 non-null    float64\n",
            " 10           winddirection  365 non-null    float64\n",
            " 11  windspeed               365 non-null    float64\n",
            "dtypes: float64(8), int64(3), object(1)\n",
            "memory usage: 34.4+ KB\n"
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        "id": "ckunOzxZiWDT",
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          "data": {
            "text/plain": [
              "                        count         mean        std    min       25%  \\\n",
              "day                     366.0    15.756831   8.823592    1.0     8.000   \n",
              "pressure                366.0  1013.742623   6.414776  998.5  1008.500   \n",
              "maxtemp                 366.0    26.191257   5.978343    7.1    21.200   \n",
              "temparature             366.0    23.747268   5.632813    4.9    18.825   \n",
              "mintemp                 366.0    21.894536   5.594153    3.1    17.125   \n",
              "dewpoint                366.0    19.989071   5.997021   -0.4    16.125   \n",
              "humidity                366.0    80.177596  10.062470   36.0    75.000   \n",
              "cloud                   366.0    71.128415  21.798012    0.0    58.000   \n",
              "sunshine                366.0     4.419399   3.934398    0.0     0.500   \n",
              "         winddirection  365.0   101.506849  81.723724   10.0    40.000   \n",
              "windspeed               365.0    21.536986  10.069712    4.4    13.700   \n",
              "\n",
              "                            50%       75%     max  \n",
              "day                       16.00    23.000    31.0  \n",
              "pressure                1013.00  1018.100  1034.6  \n",
              "maxtemp                   27.75    31.200    36.3  \n",
              "temparature               25.45    28.600    32.4  \n",
              "mintemp                   23.70    26.575    30.0  \n",
              "dewpoint                  21.95    25.000    26.7  \n",
              "humidity                  80.50    87.000    98.0  \n",
              "cloud                     80.00    88.000   100.0  \n",
              "sunshine                   3.50     8.200    12.1  \n",
              "         winddirection    70.00   190.000   350.0  \n",
              "windspeed                 20.50    27.900    59.5  "
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
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              "      <td>15.756831</td>\n",
              "      <td>8.823592</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.000</td>\n",
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              "      <td>23.000</td>\n",
              "      <td>31.0</td>\n",
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              "    <tr>\n",
              "      <th>pressure</th>\n",
              "      <td>366.0</td>\n",
              "      <td>1013.742623</td>\n",
              "      <td>6.414776</td>\n",
              "      <td>998.5</td>\n",
              "      <td>1008.500</td>\n",
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              "      <td>1018.100</td>\n",
              "      <td>1034.6</td>\n",
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              "      <td>7.1</td>\n",
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              "      <td>4.9</td>\n",
              "      <td>18.825</td>\n",
              "      <td>25.45</td>\n",
              "      <td>28.600</td>\n",
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              "      <th>mintemp</th>\n",
              "      <td>366.0</td>\n",
              "      <td>21.894536</td>\n",
              "      <td>5.594153</td>\n",
              "      <td>3.1</td>\n",
              "      <td>17.125</td>\n",
              "      <td>23.70</td>\n",
              "      <td>26.575</td>\n",
              "      <td>30.0</td>\n",
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              "    <tr>\n",
              "      <th>dewpoint</th>\n",
              "      <td>366.0</td>\n",
              "      <td>19.989071</td>\n",
              "      <td>5.997021</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>16.125</td>\n",
              "      <td>21.95</td>\n",
              "      <td>25.000</td>\n",
              "      <td>26.7</td>\n",
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              "    <tr>\n",
              "      <th>humidity</th>\n",
              "      <td>366.0</td>\n",
              "      <td>80.177596</td>\n",
              "      <td>10.062470</td>\n",
              "      <td>36.0</td>\n",
              "      <td>75.000</td>\n",
              "      <td>80.50</td>\n",
              "      <td>87.000</td>\n",
              "      <td>98.0</td>\n",
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              "    <tr>\n",
              "      <th>cloud</th>\n",
              "      <td>366.0</td>\n",
              "      <td>71.128415</td>\n",
              "      <td>21.798012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.000</td>\n",
              "      <td>80.00</td>\n",
              "      <td>88.000</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sunshine</th>\n",
              "      <td>366.0</td>\n",
              "      <td>4.419399</td>\n",
              "      <td>3.934398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500</td>\n",
              "      <td>3.50</td>\n",
              "      <td>8.200</td>\n",
              "      <td>12.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winddirection</th>\n",
              "      <td>365.0</td>\n",
              "      <td>101.506849</td>\n",
              "      <td>81.723724</td>\n",
              "      <td>10.0</td>\n",
              "      <td>40.000</td>\n",
              "      <td>70.00</td>\n",
              "      <td>190.000</td>\n",
              "      <td>350.0</td>\n",
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              "    <tr>\n",
              "      <th>windspeed</th>\n",
              "      <td>365.0</td>\n",
              "      <td>21.536986</td>\n",
              "      <td>10.069712</td>\n",
              "      <td>4.4</td>\n",
              "      <td>13.700</td>\n",
              "      <td>20.50</td>\n",
              "      <td>27.900</td>\n",
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          "metadata": {},
          "execution_count": 29
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    {
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      "source": [
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        "id": "KmhnQTUriW-o",
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      "execution_count": null,
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              "day                       0\n",
              "pressure                  0\n",
              "maxtemp                   0\n",
              "temparature               0\n",
              "mintemp                   0\n",
              "dewpoint                  0\n",
              "humidity                  0\n",
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              "rainfall                  0\n",
              "sunshine                  0\n",
              "         winddirection    1\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pressure</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>maxtemp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>temparature</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mintemp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dewpoint</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>humidity</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cloud</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rainfall</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sunshine</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winddirection</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>windspeed</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qlluhgtiX-o",
        "outputId": "3e10382b-e707-4a07-a457-756c1c5a02eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['day', 'pressure ', 'maxtemp', 'temparature', 'mintemp', 'dewpoint',\n",
              "       'humidity ', 'cloud ', 'rainfall', 'sunshine', '         winddirection',\n",
              "       'windspeed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(str.strip,\n",
        "          axis='columns',\n",
        "          inplace=True)\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1eY1BaxTiY8O",
        "outputId": "65125e4f-d660-4118-e4ed-16030347e2ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['day', 'pressure', 'maxtemp', 'temparature', 'mintemp', 'dewpoint',\n",
              "       'humidity', 'cloud', 'rainfall', 'sunshine', 'winddirection',\n",
              "       'windspeed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "\n",
        "  # Checking if the column contains\n",
        "  # any null values\n",
        "  if df[col].isnull().sum() > 0:\n",
        "    val = df[col].mean()\n",
        "    df[col] = df[col].fillna(val)\n",
        "\n",
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYg1SqZHiZ_K",
        "outputId": "007846ce-b14e-4f7e-a001-6c301656f6ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(df['rainfall'].value_counts().values,\n",
        "        labels = df['rainfall'].value_counts().index,\n",
        "        autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "ziJpyf0iibJI",
        "outputId": "b9136ef8-e4de-49cb-ecae-1f274ac6a452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('rainfall').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "fwLpRFWNicJ4",
        "outputId": "3be0b802-6a60-4e9a-b85c-fd634b02cb12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                day     pressure    maxtemp  temparature    mintemp  \\\n",
              "rainfall                                                              \n",
              "no        15.623932  1014.576923  27.070940    24.053846  21.957265   \n",
              "yes       15.819277  1013.350602  25.777912    23.603213  21.865060   \n",
              "\n",
              "           dewpoint   humidity      cloud  sunshine  winddirection  windspeed  \n",
              "rainfall                                                                       \n",
              "no        18.766667  73.000000  51.256410  7.586325     103.076923  19.275214  \n",
              "yes       20.563454  83.550201  80.465863  2.931325     100.769104  22.599747  "
            ],
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              "      <th>no</th>\n",
              "      <td>15.623932</td>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-64fbb1a4-3889-47a5-a396-ab36f1559e70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"rainfall\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1381301167656143,\n        \"min\": 15.623931623931623,\n        \"max\": 15.819277108433734,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          15.819277108433734,\n          15.623931623931623\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8671396597461102,\n        \"min\": 1013.3506024096386,\n        \"max\": 1014.5769230769231,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1013.3506024096386,\n          1014.5769230769231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"maxtemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9143092378382276,\n        \"min\": 25.777911646586343,\n        \"max\": 27.070940170940172,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          25.777911646586343,\n          27.070940170940172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temparature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3186458639841892,\n        \"min\": 23.603212851405623,\n        \"max\": 24.053846153846155,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          23.603212851405623,\n          24.053846153846155\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mintemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06519858015389271,\n        \"min\": 21.865060240963853,\n        \"max\": 21.957264957264957,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          21.865060240963853,\n          21.957264957264957\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dewpoint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2705203771199267,\n        \"min\": 18.766666666666666,\n        \"max\": 20.563453815261045,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          20.563453815261045,\n          18.766666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.460118530831563,\n        \"min\": 73.0,\n        \"max\": 83.55020080321285,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          83.55020080321285,\n          73.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cloud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.65420243063616,\n        \"min\": 51.256410256410255,\n        \"max\": 80.46586345381526,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          80.46586345381526,\n          51.256410256410255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sunshine\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.2915817023482163,\n        \"min\": 2.931325301204819,\n        \"max\": 7.586324786324787,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.931325301204819,\n          7.586324786324787\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"winddirection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6318746516192204,\n        \"min\": 100.76910381251031,\n        \"max\": 103.07692307692308,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100.76910381251031,\n          103.07692307692308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3508000108165272,\n        \"min\": 19.275213675213674,\n        \"max\": 22.599746932937226,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          22.599746932937226,\n          19.275213675213674\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = list(df.select_dtypes(include = np.number).columns)\n",
        "features.remove('day')\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dbfo1L8QidGs",
        "outputId": "7eea132f-f915-4684-f6f2-bab37717251d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['pressure', 'maxtemp', 'temparature', 'mintemp', 'dewpoint', 'humidity', 'cloud', 'sunshine', 'winddirection', 'windspeed']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(15,8))\n",
        "\n",
        "for i, col in enumerate(features):\n",
        "  plt.subplot(3,4, i + 1)\n",
        "  sb.distplot(df[col])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8A1pSDoieFs",
        "outputId": "90093b2f-f145-4993-dc00-9259479b16ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(15,8))\n",
        "\n",
        "for i, col in enumerate(features):\n",
        "  plt.subplot(3,4, i + 1)\n",
        "  sb.boxplot(df[col])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDJpUUZDifWK",
        "outputId": "ef3e39c7-55f6-4419-91c3-3ee30dabf987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 10 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace({'yes':1, 'no':0}, inplace=True)"
      ],
      "metadata": {
        "id": "P53omedKijYd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sb.heatmap(df.corr() > 0.8,\n",
        "           annot=True,\n",
        "           cbar=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "L5Y6T8PvildD",
        "outputId": "0e184f54-1233-4953-af85-c4d795ec320d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['maxtemp', 'mintemp'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "qC1QU5LYioYE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = df.drop(['day', 'rainfall'], axis=1)\n",
        "target = df.rainfall"
      ],
      "metadata": {
        "id": "-asugljTiphV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, Y_train, Y_val = train_test_split(features,\n",
        "                                      target,\n",
        "                                      test_size=0.2,\n",
        "                                      stratify=target,\n",
        "                                      random_state=2)\n",
        "\n",
        "# As the data was highly imbalanced we will\n",
        "# balance it by adding repetitive rows of minority class.\n",
        "ros = RandomOverSampler(sampling_strategy='minority',\n",
        "                        random_state=22)\n",
        "X, Y = ros.fit_resample(X_train, Y_train)"
      ],
      "metadata": {
        "id": "acahjL1eiqZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing the features for stable and fast training.\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "X_val = scaler.transform(X_val)"
      ],
      "metadata": {
        "id": "Jj_Mi8tJisqB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [LogisticRegression(), XGBClassifier(), SVC(kernel='rbf', probability=True)]\n",
        "\n",
        "for i in range(3):\n",
        "  models[i].fit(X, Y)\n",
        "\n",
        "  print(f'{models[i]} : ')\n",
        "\n",
        "  train_preds = models[i].predict_proba(X)\n",
        "  print('Training Accuracy : ', metrics.roc_auc_score(Y, train_preds[:,1]))\n",
        "\n",
        "  val_preds = models[i].predict_proba(X_val)\n",
        "  print('Validation Accuracy : ', metrics.roc_auc_score(Y_val, val_preds[:,1]))\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYNkHkgKit0m",
        "outputId": "515aeb29-51a1-46a4-e1db-132091a0ba6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression() : \n",
            "Training Accuracy :  0.8893967324057473\n",
            "Validation Accuracy :  0.8966666666666667\n",
            "\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
            "              num_parallel_tree=None, random_state=None, ...) : \n",
            "Training Accuracy :  0.9999999999999999\n",
            "Validation Accuracy :  0.8391666666666666\n",
            "\n",
            "SVC(probability=True) : \n",
            "Training Accuracy :  0.9026413474407212\n",
            "Validation Accuracy :  0.8858333333333333\n",
            "\n"
          ]
        }
      ]
    },
    {
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn import metrics\n",
        "\n",
        "# Use ConfusionMatrixDisplay.from_estimator instead of plot_confusion_matrix\n",
        "ConfusionMatrixDisplay.from_estimator(models[2], X_val, Y_val)\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "IAxMO6t5jBmW",
        "outputId": "38092572-57af-41a0-8071-296433a5f26d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(Y_val,\n",
        "                                    models[2].predict(X_val)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A72EMuJviwOn",
        "outputId": "92bd607b-6b36-419b-f214-d99d0c3e65af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.67      0.74        24\n",
            "           1       0.85      0.94      0.90        50\n",
            "\n",
            "    accuracy                           0.85        74\n",
            "   macro avg       0.85      0.80      0.82        74\n",
            "weighted avg       0.85      0.85      0.85        74\n",
            "\n"
          ]
        }
      ]
    }
  ]
}