ANN_Model.ipynb

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        "id": "978e5ae8-fca7-4821-a1c0-5fcad0ffaff4"
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      "source": [
        "**Importing Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "da09af5b-8249-4d89-916d-a9a0ff96437a",
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        "id": "da09af5b-8249-4d89-916d-a9a0ff96437a"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "170c1738-7b0c-4470-a48b-66a464cd9fd5",
      "metadata": {
        "id": "170c1738-7b0c-4470-a48b-66a464cd9fd5"
      },
      "source": [
        "**Importing Dataset**"
      ]
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    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c9d700dd-4658-440b-a9d1-8938915b4f2b",
      "metadata": {
        "id": "c9d700dd-4658-440b-a9d1-8938915b4f2b"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"/content/dataset_after_full_preprocessig.csv\")"
      ]
    },
    {
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      "execution_count": 54,
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        "outputId": "da258391-6b7e-4a2a-f57f-e3784ba06f83"
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        {
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              "               Date    TC   TdC    Hr ddd  ffkmh  Precmm       P0hPa  Prechrs  \\\n",
              "0   11/3/2022 12:00  30.5  14.2  37.0   7   18.5     0.0  888.006897        6   \n",
              "1  23/06/2022 15:00  24.5  18.2  68.0   9   18.5     0.0  884.400000        3   \n",
              "2  21/06/2021 15:00  29.7  16.8  46.0   5    3.7     0.0  904.100000        3   \n",
              "3  10/10/2020 12:00  26.3  17.4  58.0   1    9.3     0.0  885.300000        6   \n",
              "4  16/08/2022 15:00  23.0  19.6  81.0   9   11.1     0.0  887.900000        3   \n",
              "\n",
              "    Nt   Nh  HKm  Viskm  Prec_rate  Classification  \n",
              "0  5.0  5.0  0.6   30.0        0.0               2  \n",
              "1  7.0  1.0  0.6   15.0        0.0               2  \n",
              "2  2.0  1.0  0.6   10.0        0.0               1  \n",
              "3  3.0  2.0  0.6   10.0        0.0               2  \n",
              "4  7.0  2.0  0.3   20.0        0.0               2  "
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              "      <th>Date</th>\n",
              "      <th>TC</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11/3/2022 12:00</td>\n",
              "      <td>30.5</td>\n",
              "      <td>14.2</td>\n",
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              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23/06/2022 15:00</td>\n",
              "      <td>24.5</td>\n",
              "      <td>18.2</td>\n",
              "      <td>68.0</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21/06/2021 15:00</td>\n",
              "      <td>29.7</td>\n",
              "      <td>16.8</td>\n",
              "      <td>46.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3.7</td>\n",
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              "      <td>0.6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10/10/2020 12:00</td>\n",
              "      <td>26.3</td>\n",
              "      <td>17.4</td>\n",
              "      <td>58.0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>885.300000</td>\n",
              "      <td>6</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>0.6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16/08/2022 15:00</td>\n",
              "      <td>23.0</td>\n",
              "      <td>19.6</td>\n",
              "      <td>81.0</td>\n",
              "      <td>9</td>\n",
              "      <td>11.1</td>\n",
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              "      <td>0.3</td>\n",
              "      <td>20.0</td>\n",
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\"num_unique_values\": 40,\n        \"samples\": [\n          3.668439716,\n          3.099307159,\n          4.385542169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HKm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.525367682835202,\n        \"min\": 0.0,\n        \"max\": 2.5,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.823999837,\n          0.2,\n          0.630859375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Viskm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.37422084487806,\n        \"min\": 0.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          20.40441324,\n          18.05627706,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prec_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1434029304572007,\n        \"min\": 0.0,\n        \"max\": 461.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          0.416666667,\n          1.083333333,\n          2.833333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Classification\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "oeZV1cTaPvyb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeZV1cTaPvyb",
        "outputId": "54ea4c0a-b51b-4f74-9161-c48a7f865669"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22264, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "Ua2kDPOfP0fP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ua2kDPOfP0fP",
        "outputId": "ca5a7969-9e15-4bdf-ebcc-691dc317b464"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'TC', 'TdC', 'Hr', 'ddd', 'ffkmh', 'Precmm', 'P0hPa', 'Prechrs',\n",
              "       'Nt', 'Nh', 'HKm', 'Viskm', 'Prec_rate', 'Classification'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aa6307ac-869a-4372-90ee-2fc24eded338",
      "metadata": {
        "id": "aa6307ac-869a-4372-90ee-2fc24eded338"
      },
      "source": [
        "**Extract Numerical**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "c831b237-c1db-4c6d-9c43-21a6ac0ff6ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c831b237-c1db-4c6d-9c43-21a6ac0ff6ac",
        "outputId": "7c709900-2218-41b1-9105-42327d0589e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['TC',\n",
              " 'TdC',\n",
              " 'Hr',\n",
              " 'ffkmh',\n",
              " 'Precmm',\n",
              " 'P0hPa',\n",
              " 'Prechrs',\n",
              " 'Nt',\n",
              " 'Nh',\n",
              " 'HKm',\n",
              " 'Viskm',\n",
              " 'Prec_rate',\n",
              " 'Classification']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "num_features=[col for col in data.columns if data[col].dtype!='O']\n",
        "num_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "1e7303e9-5a68-4447-9b77-e4fdbb83759f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "1e7303e9-5a68-4447-9b77-e4fdbb83759f",
        "outputId": "811ff6df-43f2-4868-e001-87eb63a4d25a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     TC   TdC    Hr  ffkmh  Precmm       P0hPa  Prechrs   Nt   Nh  HKm  Viskm  \\\n",
              "0  30.5  14.2  37.0   18.5     0.0  888.006897        6  5.0  5.0  0.6   30.0   \n",
              "1  24.5  18.2  68.0   18.5     0.0  884.400000        3  7.0  1.0  0.6   15.0   \n",
              "2  29.7  16.8  46.0    3.7     0.0  904.100000        3  2.0  1.0  0.6   10.0   \n",
              "3  26.3  17.4  58.0    9.3     0.0  885.300000        6  3.0  2.0  0.6   10.0   \n",
              "4  23.0  19.6  81.0   11.1     0.0  887.900000        3  7.0  2.0  0.3   20.0   \n",
              "\n",
              "   Prec_rate  Classification  \n",
              "0        0.0               2  \n",
              "1        0.0               2  \n",
              "2        0.0               1  \n",
              "3        0.0               2  \n",
              "4        0.0               2  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>TC</th>\n",
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              "      <td>30.5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29.7</td>\n",
              "      <td>16.8</td>\n",
              "      <td>46.0</td>\n",
              "      <td>3.7</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.3</td>\n",
              "      <td>17.4</td>\n",
              "      <td>58.0</td>\n",
              "      <td>9.3</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>23.0</td>\n",
              "      <td>19.6</td>\n",
              "      <td>81.0</td>\n",
              "      <td>11.1</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>0.3</td>\n",
              "      <td>20.0</td>\n",
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              "type": "dataframe",
              "variable_name": "num_data",
              "summary": "{\n  \"name\": \"num_data\",\n  \"rows\": 22264,\n  \"fields\": [\n    {\n      \"column\": \"TC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.7243313662273883,\n        \"min\": 10.0,\n        \"max\": 36.0,\n        \"num_unique_values\": 267,\n        \"samples\": [\n          30.6,\n          27.0,\n          34.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TdC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4251835634960535,\n        \"min\": 0.8,\n        \"max\": 39.8,\n        \"num_unique_values\": 243,\n        \"samples\": [\n          18.4,\n          17.6,\n          7.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.05313192534546,\n        \"min\": 13.0,\n        \"max\": 184.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          57.0,\n          80.0,\n          81.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ffkmh\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.975007052312568,\n        \"min\": 0.0,\n        \"max\": 324.2,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          14.8,\n          55.6,\n          1.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precmm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.938601785236579,\n        \"min\": 0.0,\n        \"max\": 461.0,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          30.0,\n          0.889646465,\n          0.901585603\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P0hPa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.96033549343974,\n        \"min\": 180.5,\n        \"max\": 1007.1,\n        \"num_unique_values\": 332,\n        \"samples\": [\n          878.0,\n          876.5,\n          885.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prechrs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 24,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.96341136275825,\n        \"min\": 0.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          5.143307087,\n          4.212121212,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nh\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8576754794224783,\n        \"min\": 0.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          3.668439716,\n          3.099307159,\n          4.385542169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HKm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.525367682835202,\n        \"min\": 0.0,\n        \"max\": 2.5,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          0.823999837,\n          0.2,\n          0.630859375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Viskm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.37422084487806,\n        \"min\": 0.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          20.40441324,\n          18.05627706,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prec_rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1434029304572007,\n        \"min\": 0.0,\n        \"max\": 461.0,\n        \"num_unique_values\": 176,\n        \"samples\": [\n          0.416666667,\n          1.083333333,\n          2.833333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Classification\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "num_data = data[num_features]\n",
        "num_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8aae395c-28e5-4cfd-9c5f-0ea2fe11fd59",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8aae395c-28e5-4cfd-9c5f-0ea2fe11fd59",
        "outputId": "060a247b-d2fe-4565-8640-a0702b95c513"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 0, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "num_data['Classification'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check target class balance**"
      ],
      "metadata": {
        "id": "FT7ExeXMvleh"
      },
      "id": "FT7ExeXMvleh"
    },
    {
      "cell_type": "code",
      "source": [
        "num_data.Classification.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D48978Z_luc_",
        "outputId": "87b0d32b-5f46-4bdd-9b77-0425ecf4a31a"
      },
      "id": "D48978Z_luc_",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Classification\n",
              "2    14467\n",
              "1     7756\n",
              "0       40\n",
              "3        1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class_counts = num_data['Classification'].value_counts()\n",
        "\n",
        "plt.bar(class_counts.index, class_counts.values)\n",
        "plt.xlabel('Classification')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Class Distribution Histogram')\n",
        "plt.xticks(class_counts.index)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "mUi7DPPbl4Yc",
        "outputId": "019d78bd-821d-4db8-c3c4-0fc57475b4f7"
      },
      "id": "mUi7DPPbl4Yc",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cdd1b440-a692-4b0a-9ecc-c64da9dbeaea",
      "metadata": {
        "id": "cdd1b440-a692-4b0a-9ecc-c64da9dbeaea"
      },
      "source": [
        "**Balancing data and separate the dependent and independent features**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove instances belonging to class 0 and 3\n",
        "filtered = num_data[(num_data['Classification'] != 0) & (num_data['Classification'] != 3)]\n",
        "\n",
        "# Separate features and target variable\n",
        "X_filtered = filtered.drop(columns=['Classification'])\n",
        "y_filtered = filtered['Classification']\n"
      ],
      "metadata": {
        "id": "dhWIF0l7mAny"
      },
      "id": "dhWIF0l7mAny",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filtered['Classification'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkkfUhY0mFu_",
        "outputId": "eff73e96-c2f8-4ab9-c701-a1d2ca383228"
      },
      "id": "nkkfUhY0mFu_",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Apply under sampling to balance the classes**"
      ],
      "metadata": {
        "id": "PFN-BrVnvtiS"
      },
      "id": "PFN-BrVnvtiS"
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary libraries\n",
        "from imblearn.under_sampling import RandomUnderSampler"
      ],
      "metadata": {
        "id": "HpiEQNqKmJ1D"
      },
      "id": "HpiEQNqKmJ1D",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a RandomUnderSampler object\n",
        "rus = RandomUnderSampler(random_state=42, sampling_strategy = 'majority')"
      ],
      "metadata": {
        "id": "GKwV5T06nT7-"
      },
      "id": "GKwV5T06nT7-",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Balancing the data\n",
        "X_resampled, y_X_resampled = rus.fit_resample(X_filtered, y_filtered)"
      ],
      "metadata": {
        "id": "sSBt7pWWnesk"
      },
      "id": "sSBt7pWWnesk",
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "class_counts = y_X_resampled.value_counts()\n",
        "\n",
        "plt.bar(class_counts.index, class_counts.values)\n",
        "plt.xlabel('Classification')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Class Distribution Histogram')\n",
        "plt.xticks(class_counts.index)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "H07G4DsjpmWB",
        "outputId": "954b7ccb-5b0b-4cee-ba7d-312a24a66fed"
      },
      "id": "H07G4DsjpmWB",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_X_resampled.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1rey3kAvDMY",
        "outputId": "c51d7bad-fc82-4a8c-ab40-43f8d7bf63df"
      },
      "id": "o1rey3kAvDMY",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Classification\n",
              "1    7756\n",
              "2    7756\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_resampled.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "J2AcZfEM4Bvy",
        "outputId": "aa1acf26-631e-4f91-c31d-e2955cac4a5e"
      },
      "id": "J2AcZfEM4Bvy",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             count        mean        std    min    25%         50%  \\\n",
              "TC         15512.0   24.583118   3.657734   11.0   22.0   24.900000   \n",
              "TdC        15512.0   17.452521   2.370292    0.9   16.5   17.900000   \n",
              "Hr         15512.0   66.923366  16.891634   14.0   55.0   66.000000   \n",
              "ffkmh      15512.0    9.629887   9.138955    0.0    3.7    9.300000   \n",
              "Precmm     15512.0    1.134101   5.135578    0.0    0.0    0.000000   \n",
              "P0hPa      15512.0  888.538904   9.233041  180.5  884.5  888.006897   \n",
              "Prechrs    15512.0    9.318528   8.811682    3.0    3.0    6.000000   \n",
              "Nt         15512.0    5.555682   1.933371    0.0    4.0    6.000000   \n",
              "Nh         15512.0    3.156460   1.850702    0.0    2.0    3.000000   \n",
              "HKm        15512.0    0.684209   0.488900    0.0    0.6    0.600000   \n",
              "Viskm      15512.0   17.737180   8.183681    0.0   10.0   20.000000   \n",
              "Prec_rate  15512.0    0.090879   0.459169    0.0    0.0    0.000000   \n",
              "\n",
              "                  75%          max  \n",
              "TC          27.200000    36.000000  \n",
              "TdC         18.900000    31.300000  \n",
              "Hr          80.000000   184.000000  \n",
              "ffkmh       13.000000   181.600000  \n",
              "Precmm       0.000000   106.000000  \n",
              "P0hPa      888.006897  1004.100000  \n",
              "Prechrs     24.000000    24.000000  \n",
              "Nt           7.000000     9.000000  \n",
              "Nh           4.000000     9.000000  \n",
              "HKm          0.600000     2.500000  \n",
              "Viskm       25.000000    45.000000  \n",
              "Prec_rate    0.000000     9.666667  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-274dfa06-0879-48e5-a859-b0049ca21180\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TC</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>24.583118</td>\n",
              "      <td>3.657734</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>24.900000</td>\n",
              "      <td>27.200000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TdC</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>17.452521</td>\n",
              "      <td>2.370292</td>\n",
              "      <td>0.9</td>\n",
              "      <td>16.5</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>18.900000</td>\n",
              "      <td>31.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hr</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>66.923366</td>\n",
              "      <td>16.891634</td>\n",
              "      <td>14.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>184.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ffkmh</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>9.629887</td>\n",
              "      <td>9.138955</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>9.300000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>181.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precmm</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>1.134101</td>\n",
              "      <td>5.135578</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>106.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P0hPa</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>888.538904</td>\n",
              "      <td>9.233041</td>\n",
              "      <td>180.5</td>\n",
              "      <td>884.5</td>\n",
              "      <td>888.006897</td>\n",
              "      <td>888.006897</td>\n",
              "      <td>1004.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prechrs</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>9.318528</td>\n",
              "      <td>8.811682</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>24.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nt</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>5.555682</td>\n",
              "      <td>1.933371</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nh</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>3.156460</td>\n",
              "      <td>1.850702</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HKm</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>0.684209</td>\n",
              "      <td>0.488900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Viskm</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>17.737180</td>\n",
              "      <td>8.183681</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prec_rate</th>\n",
              "      <td>15512.0</td>\n",
              "      <td>0.090879</td>\n",
              "      <td>0.459169</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-274dfa06-0879-48e5-a859-b0049ca21180');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-f5105145-0ac8-4b55-8ee7-5512561b8f53 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\": \"X_resampled\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 15512.0,\n        \"max\": 15512.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          15512.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 253.06443927029858,\n        \"min\": 0.09087927670197267,\n        \"max\": 888.5389040981112,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          17.73717953023137\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.90464083906227,\n        \"min\": 0.4591687284987263,\n        \"max\": 16.891633697442526,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          8.183680668893466\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.56904462598045,\n        \"min\": 0.0,\n        \"max\": 180.5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.75111890169862,\n        \"min\": 0.0,\n        \"max\": 884.5,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          884.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.9806102321753,\n        \"min\": 0.0,\n        \"max\": 888.0068969,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.0635684273546,\n        \"min\": 0.0,\n        \"max\": 888.0068969,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          888.0068969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 280.6971528315462,\n        \"min\": 2.5,\n        \"max\": 1004.1,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1004.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6fa9d78c-f6b5-4cf4-af83-a16150933305",
      "metadata": {
        "id": "6fa9d78c-f6b5-4cf4-af83-a16150933305"
      },
      "source": [
        "**Splitting Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "23ad3a2c-e622-4f88-b0f2-69c3cb64af73",
      "metadata": {
        "id": "23ad3a2c-e622-4f88-b0f2-69c3cb64af73"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X_resampled,y_X_resampled,train_size=0.8,random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "d1c64932-b817-4b93-9d30-29eefbeb8527",
      "metadata": {
        "id": "d1c64932-b817-4b93-9d30-29eefbeb8527"
      },
      "outputs": [],
      "source": [
        "#Import Libraries\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "6700b5b6-0eb0-48cc-91b2-56bc93b288de",
      "metadata": {
        "id": "6700b5b6-0eb0-48cc-91b2-56bc93b288de"
      },
      "outputs": [],
      "source": [
        "#Creating a StandardScaler object to normalize the X train and test set feature data\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "bff61a05-f164-422a-8e0e-3efa9965f0ca",
      "metadata": {
        "id": "bff61a05-f164-422a-8e0e-3efa9965f0ca"
      },
      "outputs": [],
      "source": [
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "c8608748-a85e-4c00-85f1-7d39b0ff603f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8608748-a85e-4c00-85f1-7d39b0ff603f",
        "outputId": "a94e38f2-cf05-4ee0-cff6-0ed85261cfcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(12409, 12)\n",
            "(12409, 1)\n",
            "(3103, 12)\n",
            "(3103,)\n"
          ]
        }
      ],
      "source": [
        "#Make sure both arrays have correct dimensions\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c5cd77f8-9861-4899-a584-5d9d55589057",
      "metadata": {
        "id": "c5cd77f8-9861-4899-a584-5d9d55589057"
      },
      "source": [
        "**Building ANN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "90d77a4e-0625-4a97-8730-c3e24d41bd60",
      "metadata": {
        "id": "90d77a4e-0625-4a97-8730-c3e24d41bd60"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import BatchNormalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "023b7bd6-fbe6-49ad-b7b5-d3d331e0383c",
      "metadata": {
        "id": "023b7bd6-fbe6-49ad-b7b5-d3d331e0383c"
      },
      "outputs": [],
      "source": [
        "#initializing ann\n",
        "model = Sequential()\n",
        "#adding the first input layer and the first hidden layer\n",
        "model.add(Dense(units=32, activation='relu', input_dim=(12)))\n",
        "#add hidden layer with dropout\n",
        "model.add(Dense(units=24, activation='relu'))\n",
        "model.add(Dropout(.2))\n",
        "#add  hidden layer with dropout\n",
        "model.add(Dense(units=11, activation='relu'))\n",
        "model.add(Dropout(.2))\n",
        "#add  hidden layer\n",
        "model.add(Dense(units=8, activation='relu'))\n",
        "#add an output layer ( 4 classes in the target variable )\n",
        "model.add(Dense(units=4, activation='softmax'))\n",
        "#compile the model\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=\"adam\", metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2fcb134-af5c-463a-b8fd-48f672316421",
      "metadata": {
        "id": "b2fcb134-af5c-463a-b8fd-48f672316421"
      },
      "source": [
        "**Train the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "2fdefea2-7fd7-44f5-8c6f-1c4f8c623f6e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fdefea2-7fd7-44f5-8c6f-1c4f8c623f6e",
        "outputId": "6a1279f8-5202-4248-fb26-a83c40f5dd74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "414/414 [==============================] - 2s 2ms/step - loss: 0.7093 - accuracy: 0.6219\n",
            "Epoch 2/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.5348 - accuracy: 0.7074\n",
            "Epoch 3/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.4907 - accuracy: 0.7440\n",
            "Epoch 4/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.4664 - accuracy: 0.7601\n",
            "Epoch 5/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.4410 - accuracy: 0.7868\n",
            "Epoch 6/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.4256 - accuracy: 0.8020\n",
            "Epoch 7/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3985 - accuracy: 0.8147\n",
            "Epoch 8/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.3917 - accuracy: 0.8230\n",
            "Epoch 9/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.3807 - accuracy: 0.8300\n",
            "Epoch 10/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.3700 - accuracy: 0.8323\n",
            "Epoch 11/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3634 - accuracy: 0.8387\n",
            "Epoch 12/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3516 - accuracy: 0.8408\n",
            "Epoch 13/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3428 - accuracy: 0.8459\n",
            "Epoch 14/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3349 - accuracy: 0.8487\n",
            "Epoch 15/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3313 - accuracy: 0.8504\n",
            "Epoch 16/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3312 - accuracy: 0.8547\n",
            "Epoch 17/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3222 - accuracy: 0.8533\n",
            "Epoch 18/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3177 - accuracy: 0.8574\n",
            "Epoch 19/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3098 - accuracy: 0.8590\n",
            "Epoch 20/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3073 - accuracy: 0.8630\n",
            "Epoch 21/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.3046 - accuracy: 0.8653\n",
            "Epoch 22/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2958 - accuracy: 0.8696\n",
            "Epoch 23/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2941 - accuracy: 0.8684\n",
            "Epoch 24/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2944 - accuracy: 0.8706\n",
            "Epoch 25/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2905 - accuracy: 0.8733\n",
            "Epoch 26/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2893 - accuracy: 0.8728\n",
            "Epoch 27/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2824 - accuracy: 0.8788\n",
            "Epoch 28/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2776 - accuracy: 0.8800\n",
            "Epoch 29/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2814 - accuracy: 0.8761\n",
            "Epoch 30/100\n",
            "414/414 [==============================] - 2s 4ms/step - loss: 0.2776 - accuracy: 0.8807\n",
            "Epoch 31/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2754 - accuracy: 0.8834\n",
            "Epoch 32/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2774 - accuracy: 0.8795\n",
            "Epoch 33/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2714 - accuracy: 0.8819\n",
            "Epoch 34/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2675 - accuracy: 0.8818\n",
            "Epoch 35/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2738 - accuracy: 0.8805\n",
            "Epoch 36/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2681 - accuracy: 0.8840\n",
            "Epoch 37/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2645 - accuracy: 0.8853\n",
            "Epoch 38/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2667 - accuracy: 0.8830\n",
            "Epoch 39/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2632 - accuracy: 0.8861\n",
            "Epoch 40/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2608 - accuracy: 0.8842\n",
            "Epoch 41/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2609 - accuracy: 0.8881\n",
            "Epoch 42/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2581 - accuracy: 0.8881\n",
            "Epoch 43/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2614 - accuracy: 0.8855\n",
            "Epoch 44/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2596 - accuracy: 0.8864\n",
            "Epoch 45/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2611 - accuracy: 0.8865\n",
            "Epoch 46/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2557 - accuracy: 0.8876\n",
            "Epoch 47/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2564 - accuracy: 0.8906\n",
            "Epoch 48/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2571 - accuracy: 0.8896\n",
            "Epoch 49/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2519 - accuracy: 0.8916\n",
            "Epoch 50/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2588 - accuracy: 0.8950\n",
            "Epoch 51/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2502 - accuracy: 0.8886\n",
            "Epoch 52/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2577 - accuracy: 0.8894\n",
            "Epoch 53/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2476 - accuracy: 0.8943\n",
            "Epoch 54/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2481 - accuracy: 0.8938\n",
            "Epoch 55/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2486 - accuracy: 0.8919\n",
            "Epoch 56/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2453 - accuracy: 0.8918\n",
            "Epoch 57/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2449 - accuracy: 0.8935\n",
            "Epoch 58/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2428 - accuracy: 0.8967\n",
            "Epoch 59/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2447 - accuracy: 0.8960\n",
            "Epoch 60/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2486 - accuracy: 0.8935\n",
            "Epoch 61/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2417 - accuracy: 0.8964\n",
            "Epoch 62/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2476 - accuracy: 0.8955\n",
            "Epoch 63/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2421 - accuracy: 0.8951\n",
            "Epoch 64/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2465 - accuracy: 0.8939\n",
            "Epoch 65/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2437 - accuracy: 0.8968\n",
            "Epoch 66/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2379 - accuracy: 0.8982\n",
            "Epoch 67/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2404 - accuracy: 0.8973\n",
            "Epoch 68/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2412 - accuracy: 0.8975\n",
            "Epoch 69/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2510 - accuracy: 0.8958\n",
            "Epoch 70/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2342 - accuracy: 0.9031\n",
            "Epoch 71/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2401 - accuracy: 0.8981\n",
            "Epoch 72/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2356 - accuracy: 0.9002\n",
            "Epoch 73/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2374 - accuracy: 0.8985\n",
            "Epoch 74/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2309 - accuracy: 0.9000\n",
            "Epoch 75/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2429 - accuracy: 0.8969\n",
            "Epoch 76/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2373 - accuracy: 0.8998\n",
            "Epoch 77/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2356 - accuracy: 0.9004\n",
            "Epoch 78/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2294 - accuracy: 0.9039\n",
            "Epoch 79/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2335 - accuracy: 0.9002\n",
            "Epoch 80/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2359 - accuracy: 0.9009\n",
            "Epoch 81/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2319 - accuracy: 0.9004\n",
            "Epoch 82/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2294 - accuracy: 0.9031\n",
            "Epoch 83/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2284 - accuracy: 0.9051\n",
            "Epoch 84/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2249 - accuracy: 0.9051\n",
            "Epoch 85/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2300 - accuracy: 0.9020\n",
            "Epoch 86/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2313 - accuracy: 0.9045\n",
            "Epoch 87/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2288 - accuracy: 0.9027\n",
            "Epoch 88/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2284 - accuracy: 0.9050\n",
            "Epoch 89/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2261 - accuracy: 0.9054\n",
            "Epoch 90/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2273 - accuracy: 0.9031\n",
            "Epoch 91/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2247 - accuracy: 0.9056\n",
            "Epoch 92/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2281 - accuracy: 0.9039\n",
            "Epoch 93/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2294 - accuracy: 0.9051\n",
            "Epoch 94/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2205 - accuracy: 0.9071\n",
            "Epoch 95/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2267 - accuracy: 0.9039\n",
            "Epoch 96/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2311 - accuracy: 0.9047\n",
            "Epoch 97/100\n",
            "414/414 [==============================] - 1s 2ms/step - loss: 0.2225 - accuracy: 0.9069\n",
            "Epoch 98/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2304 - accuracy: 0.9068\n",
            "Epoch 99/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2235 - accuracy: 0.9048\n",
            "Epoch 100/100\n",
            "414/414 [==============================] - 1s 3ms/step - loss: 0.2277 - accuracy: 0.9035\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b283d211d50>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "model.fit(X_train, y_train, epochs=100, batch_size=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "60caab31-f1af-469a-8281-3b77a5f56a5d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60caab31-f1af-469a-8281-3b77a5f56a5d",
        "outputId": "947a9841-558e-4912-be2d-e493eb12d7e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97/97 [==============================] - 0s 2ms/step - loss: 0.2423 - accuracy: 0.9152\n",
            "Accuracy of model is 0.9152433276176453\n"
          ]
        }
      ],
      "source": [
        "acc = model.evaluate(X_test, y_test)[1]\n",
        "\n",
        "print(f'Accuracy of model is {acc}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f98d5c4-d462-4601-aced-9ad0cf4f5ac4",
      "metadata": {
        "id": "3f98d5c4-d462-4601-aced-9ad0cf4f5ac4"
      },
      "source": [
        "**Print the actual and predicted values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "6844c204-4fc8-4479-ae3e-5bca6e8bb705",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6844c204-4fc8-4479-ae3e-5bca6e8bb705",
        "outputId": "8275ef47-b23e-4975-a32f-833fd02933b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97/97 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 2, ..., 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "y_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "19c77b33-9d46-4ede-9f05-faaea5a480df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "19c77b33-9d46-4ede-9f05-faaea5a480df",
        "outputId": "349e0d12-eb21-4d1e-dcf0-4d2f27e98cb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Actual Value  Predicted Value\n",
              "4462              1                1\n",
              "7799              2                2\n",
              "12381             2                2\n",
              "5274              1                1\n",
              "167               1                1\n",
              "...             ...              ...\n",
              "660               1                1\n",
              "6439              1                2\n",
              "8960              2                2\n",
              "4291              1                2\n",
              "8716              2                2\n",
              "\n",
              "[3103 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f6c7746-94b1-4814-8ef4-63a9d438aced\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Value</th>\n",
              "      <th>Predicted Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4462</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7799</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12381</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5274</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>660</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6439</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8960</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4291</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8716</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3103 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f6c7746-94b1-4814-8ef4-63a9d438aced')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f6c7746-94b1-4814-8ef4-63a9d438aced button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4f6c7746-94b1-4814-8ef4-63a9d438aced');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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-d3decde0-d045-4421-8dc2-9acce9f79561 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_698e8a97-ff2e-4a89-be3c-9ab5266dc043\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_698e8a97-ff2e-4a89-be3c-9ab5266dc043 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 3103,\n  \"fields\": [\n    {\n      \"column\": \"Actual Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predicted Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "results_df = pd.DataFrame({\n",
        "    'Actual Value': y_test,\n",
        "    'Predicted Value': y_pred\n",
        "})\n",
        "\n",
        "# Display the DataFrame\n",
        "results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "a90e79b6-16db-43ba-9b3a-40a51a849812",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a90e79b6-16db-43ba-9b3a-40a51a849812",
        "outputId": "353836a0-103c-4783-8142-43c46792967a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "97/97 [==============================] - 0s 1ms/step - loss: 0.2423 - accuracy: 0.9152\n",
            "Accuracy of model is 0.9152433276176453\n"
          ]
        }
      ],
      "source": [
        "acc = model.evaluate(X_test, y_test)[1]\n",
        "\n",
        "print(f'Accuracy of model is {acc}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "zhi6iTj-qR1n",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zhi6iTj-qR1n",
        "outputId": "81a75b7e-f8d0-4975-f778-3d1bcaa4c913"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.90      0.93      0.92      1535\n",
            "           2       0.93      0.90      0.91      1568\n",
            "\n",
            "    accuracy                           0.92      3103\n",
            "   macro avg       0.92      0.92      0.92      3103\n",
            "weighted avg       0.92      0.92      0.92      3103\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "NT05QohezzJ9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NT05QohezzJ9",
        "outputId": "325716f7-6741-4f23-fba4-b139b7d3dafe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                416       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 24)                792       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 24)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 11)                275       \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 11)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8)                 96        \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4)                 36        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1615 (6.31 KB)\n",
            "Trainable params: 1615 (6.31 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}