Stock_Price_Prediction.ipynb

{
  "cells": [
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      "execution_count": 1,
      "metadata": {
        "id": "qCDSjVhXLr_Z"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOQbXSiB-g5G",
        "outputId": "55ee6ec8-9146-4ebf-c2fe-b4b06bc3a5c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "id": "Sc4id6VxL8BS",
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              "         Date       Open       High        Low      Close  Adj Close  \\\n",
              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
              "\n",
              "       Volume  \n",
              "0  43733533.0  \n",
              "1  56167280.0  \n",
              "2  68296318.0  \n",
              "3  86073880.0  \n",
              "4  76613039.0  "
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          "execution_count": 6,
          "metadata": {},
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      "source": [
        "# Load the dataset\n",
        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "7LaYGXsfN-8y"
      },
      "outputs": [],
      "source": [
        "# Drop the 'Date' and 'Adj Close' columns\n",
        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
      ]
    },
    {
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              "      <td>18.823240</td>\n",
              "      <td>43733533.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.894005</td>\n",
              "      <td>18.964767</td>\n",
              "      <td>17.738192</td>\n",
              "      <td>18.224106</td>\n",
              "      <td>56167280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.327892</td>\n",
              "      <td>18.568489</td>\n",
              "      <td>17.643839</td>\n",
              "      <td>17.738192</td>\n",
              "      <td>68296318.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17.502312</td>\n",
              "      <td>17.832542</td>\n",
              "      <td>17.223972</td>\n",
              "      <td>17.676863</td>\n",
              "      <td>86073880.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.738192</td>\n",
              "      <td>17.785366</td>\n",
              "      <td>17.459852</td>\n",
              "      <td>17.577793</td>\n",
              "      <td>76613039.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17e410bf-3066-44ca-82ac-604953651c3e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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-17e410bf-3066-44ca-82ac-604953651c3e 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-17e410bf-3066-44ca-82ac-604953651c3e');\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",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-014618c6-10b7-4818-a9f6-f74d64a18bed')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\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-014618c6-10b7-4818-a9f6-f74d64a18bed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        Open       High        Low      Close      Volume\n",
              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "dydEPoNeM6eN"
      },
      "outputs": [],
      "source": [
        "# Handle missing values\n",
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(strategy='mean')\n",
        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "OQ3cGqgTMBwt"
      },
      "outputs": [],
      "source": [
        "# Select features and target variable\n",
        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
        "y = df['Close']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9Oz-bwJOMEWD"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ugapDyXODtn3"
      },
      "outputs": [],
      "source": [
        "# Scale the features using Min-Max scaling\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "997ZEgibCZIO",
        "outputId": "879af72c-3e7c-4b8c-ba28-452fa8ba3cd6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5659, 4)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmtt76RuCeyG",
        "outputId": "855f34ec-4914-4e1f-c9b1-6f8753612100"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1415, 4)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CeJkUJ92Ciqd",
        "outputId": "21c5949c-e787-40df-b816-7cc9e830e401"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5659,)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7HGC7VuTCjWc",
        "outputId": "cbda22d6-bec9-45d7-80ac-c74ad14b7c0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1415,)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6Ek8jRlO2_I"
      },
      "source": [
        "## 1. LINEAR REGRESSION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "RdZ1SpzdMHAJ"
      },
      "outputs": [],
      "source": [
        "# Create a linear regression model\n",
        "model1 = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "mPM035IzMY04",
        "outputId": "084d9520-1d31-4287-a9d1-6f9eeea60f9b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5286</th>\n",
              "      <td>257.350006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3408</th>\n",
              "      <td>129.464996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5477</th>\n",
              "      <td>279.350006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6906</th>\n",
              "      <td>588.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>21.644367</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ],
            "text/plain": [
              "5286    257.350006\n",
              "3408    129.464996\n",
              "5477    279.350006\n",
              "6906    588.500000\n",
              "530      21.644367\n",
              "Name: Close, dtype: float64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "qBhQ9HbYMI3d",
        "outputId": "daac2bc7-a480-4013-8e06-717bb8c1fa65"
      },
      "outputs": [
        {
          "data": {
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              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
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              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
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              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
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              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model1.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "X269co2kMS4z"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred1 = model1.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "QK8GvDYPOd0Y"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
        "mae1 = mean_absolute_error(y_test, pred1)\n",
        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEi49xtEOtne",
        "outputId": "60b53406-81c4-4531-cbb0-77b9ffde8890"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 1.6881364643681482\n",
            "MAE: 0.9433353485344729\n",
            "MAPE: 0.006085435990853812\n",
            "Accuracy: 0.8296819787985866\n",
            "Precision: 0.8623595505617978\n",
            "Confusion Matrix:\n",
            " [[560  98]\n",
            " [143 614]]\n",
            "Recall: 0.8110964332892999\n",
            "F1 Score: 0.8359428182437032\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse1)\n",
        "print(\"MAE:\", mae1)\n",
        "print(\"MAPE:\", mape1)\n",
        "print(\"Accuracy:\", accuracy1)\n",
        "print(\"Precision:\", precision1)\n",
        "print(\"Confusion Matrix:\\n\", confusion1)\n",
        "print(\"Recall:\", recall1)\n",
        "print(\"F1 Score:\", f11)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxtMzlg-gR2P"
      },
      "source": [
        "## 2. SVR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "o7K9r7EXWRjQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVR\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "0xQewd7QWTtq"
      },
      "outputs": [],
      "source": [
        "# Create an SVR model\n",
        "model2 = SVR()\n",
        "param_grid = {'C':[0.1, 1], 'epsilon':[0.01, 0.1, 0.5], 'kernel':['sigmoid']}\n",
        "GV_SVR = GridSearchCV(model2, param_grid = param_grid, scoring = 'accuracy', n_jobs = -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "DuNes3s6U2IV"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "22SaCsQmfhgP",
        "outputId": "7867aa8e-a7dc-49e4-d0aa-310423e9c696"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:1103: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1], &#x27;epsilon&#x27;: [0.01, 0.1, 0.5],\n",
              "                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1], &#x27;epsilon&#x27;: [0.01, 0.1, 0.5],\n",
              "                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVR</label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=0.1, epsilon=0.01, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=0.1, epsilon=0.01, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
              "             param_grid={'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5],\n",
              "                         'kernel': ['sigmoid']},\n",
              "             scoring='accuracy')"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "GV_SVR.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "OQ1nL4oYfkAC"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred2 = GV_SVR.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6-EppR68Jsu",
        "outputId": "cfa67a46-ab8c-478d-c13e-fe884ef7ebae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'C': 0.1, 'epsilon': 0.01, 'kernel': 'sigmoid'}"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "GV_SVR.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "nRYTwydsfpjb"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics with grid search\n",
        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
        "mae2 = mean_absolute_error(y_test, pred2)\n",
        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "656J5oz5fzq6",
        "outputId": "c62c2076-2a10-43da-935d-c593c2d7be79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 158.5191296342117\n",
            "MAE: 127.61818666535594\n",
            "MAPE: 2.6724681150799072\n",
            "Accuracy: 0.9985865724381625\n",
            "Precision: 0.9970457902511078\n",
            "Confusion Matrix:\n",
            " [[738   2]\n",
            " [  0 675]]\n",
            "Recall: 1.0\n",
            "F1 Score: 0.9985207100591716\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics with grid search\n",
        "print(\"RMSE:\", rmse2)\n",
        "print(\"MAE:\", mae2)\n",
        "print(\"MAPE:\", mape2)\n",
        "print(\"Accuracy:\", accuracy2)\n",
        "print(\"Precision:\", precision2)\n",
        "print(\"Confusion Matrix:\\n\", confusion2)\n",
        "print(\"Recall:\", recall2)\n",
        "print(\"F1 Score:\", f12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "tIlc-wCE8Jsv",
        "outputId": "fc6a39f5-b546-444e-a522-8bbcf889be14"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "SVR()"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#fitting without grid search\n",
        "model2.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "f3u1h6Dk8Jsv"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred2_1 = model2.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "F-NgJx2d8Jsv"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics without grid search\n",
        "rmse2_1 = np.sqrt(mean_squared_error(y_test, pred2_1))\n",
        "mae2_1 = mean_absolute_error(y_test, pred2_1)\n",
        "mape2_1 = mean_absolute_percentage_error(y_test, pred2_1)\n",
        "accuracy2_1 = accuracy_score(y_test > pred2_1, y_test > pred2_1.round())\n",
        "precision2_1 = precision_score(y_test > pred2_1, y_test > pred2_1.round())\n",
        "confusion2_1 = confusion_matrix(y_test > pred2_1, y_test > pred2_1.round())\n",
        "recall2_1 = recall_score(y_test > pred2_1, y_test > pred2_1.round())\n",
        "f12_1 = f1_score(y_test > pred2_1, y_test > pred2_1.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_BB1EERE8Jsv",
        "outputId": "11c94daf-d2db-4447-a5b9-b753e648b56c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 147.71103599153602\n",
            "MAE: 110.99419106508152\n",
            "MAPE: 1.9715076513294716\n",
            "Accuracy: 0.9992932862190813\n",
            "Precision: 1.0\n",
            "Confusion Matrix:\n",
            " [[727   0]\n",
            " [  1 687]]\n",
            "Recall: 0.998546511627907\n",
            "F1 Score: 0.9992727272727273\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics without grid search\n",
        "print(\"RMSE:\", rmse2_1)\n",
        "print(\"MAE:\", mae2_1)\n",
        "print(\"MAPE:\", mape2_1)\n",
        "print(\"Accuracy:\", accuracy2_1)\n",
        "print(\"Precision:\", precision2_1)\n",
        "print(\"Confusion Matrix:\\n\", confusion2_1)\n",
        "print(\"Recall:\", recall2_1)\n",
        "print(\"F1 Score:\", f12_1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcIfVMWdgcKt"
      },
      "source": [
        "## 3. Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "f7raXT_hf2ij"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "# Create a Random Forest model\n",
        "model3 = RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "TadNM7MEU7fh"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "fF002Yepgk55",
        "outputId": "4ff3a76e-04be-452e-8927-a7739bc306fb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model3.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "8nRU_pzEgnCt"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred3 = model3.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "4aKEXGVUgsry"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
        "mae3 = mean_absolute_error(y_test, pred3)\n",
        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pPzsCY1g305",
        "outputId": "bc5eeab1-5d75-4be3-b26f-70c4fe533a47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 2.2227552163641375\n",
            "MAE: 1.2618627107561857\n",
            "MAPE: 0.008011786688180922\n",
            "Accuracy: 0.8537102473498234\n",
            "Precision: 0.8575624082232012\n",
            "Confusion Matrix:\n",
            " [[624  97]\n",
            " [110 584]]\n",
            "Recall: 0.8414985590778098\n",
            "F1 Score: 0.8494545454545455\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse3)\n",
        "print(\"MAE:\", mae3)\n",
        "print(\"MAPE:\", mape3)\n",
        "print(\"Accuracy:\", accuracy3)\n",
        "print(\"Precision:\", precision3)\n",
        "print(\"Confusion Matrix:\\n\", confusion3)\n",
        "print(\"Recall:\", recall3)\n",
        "print(\"F1 Score:\", f13)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZsLwLivhLGH"
      },
      "source": [
        "## 4. Gradient Boosting Models (GBM)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "TI8idoxOg6jF"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb\n",
        "# Create an XGBoost model\n",
        "model4 = xgb.XGBRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "7r9xJDtOVBEA"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "2gpbDxshhexj",
        "outputId": "2cdbacc9-909a-43c9-cf04-cc4f70015e43"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(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, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(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, ...)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(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, ...)"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model4.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "Jj9DXdUPhh9V"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred4 = model4.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "TdH60Sllhn5O"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
        "mae4 = mean_absolute_error(y_test, pred4)\n",
        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qpnLeFyZhwB3",
        "outputId": "1637d31a-e785-4414-dfe5-f7fa4fd282e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 2.733930065274145\n",
            "MAE: 1.502457380471909\n",
            "MAPE: 0.010026410639661481\n",
            "Accuracy: 0.8840989399293286\n",
            "Precision: 0.8948106591865358\n",
            "Confusion Matrix:\n",
            " [[613  75]\n",
            " [ 89 638]]\n",
            "Recall: 0.8775790921595599\n",
            "F1 Score: 0.8861111111111111\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse4)\n",
        "print(\"MAE:\", mae4)\n",
        "print(\"MAPE:\", mape4)\n",
        "print(\"Accuracy:\", accuracy4)\n",
        "print(\"Precision:\", precision4)\n",
        "print(\"Confusion Matrix:\\n\", confusion4)\n",
        "print(\"Recall:\", recall4)\n",
        "print(\"F1 Score:\", f14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8nSGoyuh9dx"
      },
      "source": [
        "## 5. Extreme Gradient Boosting (XGBoost)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "DyhhdlZAhx94"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb\n",
        "# Create an XGBoost model\n",
        "model5 = xgb.XGBRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "Z_AD0lVOVHwB"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "RAIwxIp5iH9Z",
        "outputId": "cc66ca33-11cc-4e5d-e7e1-f39b41466c28"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-6 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-6 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-6 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-6 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-6 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-6 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(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, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(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, ...)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(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, ...)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model5.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "XmJds5fYiKT3"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred5 = model5.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "lZ1A0-L8iNCM"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
        "mae5 = mean_absolute_error(y_test, pred5)\n",
        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7IkE-RAmiWNo",
        "outputId": "d69e485e-0462-409a-f9aa-59e5ca7c4bef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 2.733930065274145\n",
            "MAE: 1.502457380471909\n",
            "MAPE: 0.010026410639661481\n",
            "Accuracy: 0.8840989399293286\n",
            "Precision: 0.8948106591865358\n",
            "Confusion Matrix:\n",
            " [[613  75]\n",
            " [ 89 638]]\n",
            "Recall: 0.8775790921595599\n",
            "F1 Score: 0.8861111111111111\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse5)\n",
        "print(\"MAE:\", mae5)\n",
        "print(\"MAPE:\", mape5)\n",
        "print(\"Accuracy:\", accuracy5)\n",
        "print(\"Precision:\", precision5)\n",
        "print(\"Confusion Matrix:\\n\", confusion5)\n",
        "print(\"Recall:\", recall5)\n",
        "print(\"F1 Score:\", f15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_J776rtiovq"
      },
      "source": [
        "## 6. AdaBoostRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "HNq66cXRiYPJ"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "# Create an AdaBoost model\n",
        "model6 = AdaBoostRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "qPHH6rG0VW4V"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "P0oB5wjQivBr",
        "outputId": "827e151d-e13e-4924-ef50-c5e1d13f6161"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-7 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-7 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-7 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-7 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-7 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-7 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-7 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-7 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-7 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "AdaBoostRegressor()"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model6.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "Bf1m5ukOi2VM"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred6 = model6.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "oFWSqC4ai6gd"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
        "mae6 = mean_absolute_error(y_test, pred6)\n",
        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BsajWJGBjC80",
        "outputId": "076cff01-ff0a-449b-d86c-df9f1363fecd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 10.504089217159294\n",
            "MAE: 8.746118088217727\n",
            "MAPE: 0.20512996006219347\n",
            "Accuracy: 0.9943462897526502\n",
            "Precision: 0.9939879759519038\n",
            "Confusion Matrix:\n",
            " [[911   3]\n",
            " [  5 496]]\n",
            "Recall: 0.9900199600798403\n",
            "F1 Score: 0.992\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse6)\n",
        "print(\"MAE:\", mae6)\n",
        "print(\"MAPE:\", mape6)\n",
        "print(\"Accuracy:\", accuracy6)\n",
        "print(\"Precision:\", precision6)\n",
        "print(\"Confusion Matrix:\\n\", confusion6)\n",
        "print(\"Recall:\", recall6)\n",
        "print(\"F1 Score:\", f16)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9DzOt3CkWFX"
      },
      "source": [
        "## 7. Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "23DZ2biSjF9a"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "# Create a Decision Tree model\n",
        "model7 = DecisionTreeRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "Ajo2RAVAVb7H"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "6mQEQf-ykc9F",
        "outputId": "78dc7661-96e4-46af-b65a-c705018c58a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-8 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-8 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-8 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-8 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-8 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "DecisionTreeRegressor()"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model7.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "BFJ9q_tvkgRC"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred7 = model7.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "9IxfYZbYkjv1"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
        "mae7 = mean_absolute_error(y_test, pred7)\n",
        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnZXMYb8kooV",
        "outputId": "93527d46-9366-49ce-b895-7a12ae87f742"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 3.136698143876249\n",
            "MAE: 1.6416165234385407\n",
            "MAPE: 0.010224639636659394\n",
            "Accuracy: 0.8600706713780919\n",
            "Precision: 0.8687845303867403\n",
            "Confusion Matrix:\n",
            " [[588  95]\n",
            " [103 629]]\n",
            "Recall: 0.8592896174863388\n",
            "F1 Score: 0.864010989010989\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse7)\n",
        "print(\"MAE:\", mae7)\n",
        "print(\"MAPE:\", mape7)\n",
        "print(\"Accuracy:\", accuracy7)\n",
        "print(\"Precision:\", precision7)\n",
        "print(\"Confusion Matrix:\\n\", confusion7)\n",
        "print(\"Recall:\", recall7)\n",
        "print(\"F1 Score:\", f17)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LH-B-Xd6k5UD"
      },
      "source": [
        "## 8. KNeighborsRegressor(KNN)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "JVDSed7yktFY"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# Create a KNN model\n",
        "model8 = KNeighborsRegressor()\n",
        "param_grid = {'n_neighbors':[3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]}\n",
        "GV_KNN = GridSearchCV(model8, param_grid, cv=5, scoring='neg_mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "XJHb5SxrVgVp"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "9fn64o-ZlBka",
        "outputId": "bc388b6b-cf36-4a91-eb52-8fe7a861308b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-9 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-9 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-9 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-9 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-9 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-9 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-9 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-9 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-9 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-9 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsRegressor()"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model8.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "hbfbbjcSlDn7"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "pred8 = model8.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "hnWyNv3blHdL"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
        "mae8 = mean_absolute_error(y_test, pred8)\n",
        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPoDRkcMlMAr",
        "outputId": "5aee40b5-c7c1-4fad-f419-37d08a504146"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 148.73183825029315\n",
            "MAE: 109.35229571264969\n",
            "MAPE: 1.75024316976612\n",
            "Accuracy: 0.9908127208480565\n",
            "Precision: 0.9887820512820513\n",
            "Confusion Matrix:\n",
            " [[785   7]\n",
            " [  6 617]]\n",
            "Recall: 0.9903691813804173\n",
            "F1 Score: 0.9895749799518845\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse8)\n",
        "print(\"MAE:\", mae8)\n",
        "print(\"MAPE:\", mape8)\n",
        "print(\"Accuracy:\", accuracy8)\n",
        "print(\"Precision:\", precision8)\n",
        "print(\"Confusion Matrix:\\n\", confusion8)\n",
        "print(\"Recall:\", recall8)\n",
        "print(\"F1 Score:\", f18)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "5qW5X1Xe8Js7",
        "outputId": "6abf26c0-873e-42e2-9aab-c2bd93a5ae84"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-10 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-10 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-10 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-10 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-10 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-10 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=150)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=150)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=150)"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "GV_KNN.fit(X_train, y_train)\n",
        "pred8_1 = GV_KNN.predict(X_test)\n",
        "GV_KNN.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "voNSUlHk8Js7"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics with grid search\n",
        "rmse8_1 = np.sqrt(mean_squared_error(y_test, pred8_1))\n",
        "mae8_1 = mean_absolute_error(y_test, pred8_1)\n",
        "mape8_1 = mean_absolute_percentage_error(y_test, pred8_1)\n",
        "accuracy8_1 = accuracy_score(y_test > pred8_1, y_test > pred8_1.round())\n",
        "precision8_1 = precision_score(y_test > pred8_1, y_test > pred8_1.round())\n",
        "confusion8_1 = confusion_matrix(y_test > pred8_1, y_test > pred8_1.round())\n",
        "recall8_1 = recall_score(y_test > pred8_1, y_test > pred8_1.round())\n",
        "f18_1 = f1_score(y_test > pred8_1, y_test > pred8_1.round())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMnot_fX8Js8",
        "outputId": "71c354e6-71e6-4d31-ee2f-2a03f65d24fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 137.20694663964096\n",
            "MAE: 101.08872313712585\n",
            "MAPE: 1.7323302969000884\n",
            "Accuracy: 0.9985865724381625\n",
            "Precision: 1.0\n",
            "Confusion Matrix:\n",
            " [[865   0]\n",
            " [  2 548]]\n",
            "Recall: 0.9963636363636363\n",
            "F1 Score: 0.9981785063752276\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics with grid search\n",
        "print(\"RMSE:\", rmse8_1)\n",
        "print(\"MAE:\", mae8_1)\n",
        "print(\"MAPE:\", mape8_1)\n",
        "print(\"Accuracy:\", accuracy8_1)\n",
        "print(\"Precision:\", precision8_1)\n",
        "print(\"Confusion Matrix:\\n\", confusion8_1)\n",
        "print(\"Recall:\", recall8_1)\n",
        "print(\"F1 Score:\", f18_1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "Swebseye8Js8",
        "outputId": "c501acf3-094c-4ba6-f0d7-1a0901cdb2e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "results = GV_KNN.cv_results_\n",
        "mse = -results['mean_test_score']\n",
        "k_values = results['param_n_neighbors'].data\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(k_values, mse, marker='o', linestyle='-')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aOBO56I8Js8"
      },
      "source": [
        "The code shows that the elboq point occurs around n=30."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5XtlzMXljps"
      },
      "source": [
        "## 9. Artificial Neural Networks (ANN)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "bJk1-9VhlRL6"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "sZVPMR9Wlo7-"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd1fDjQiltP4",
        "outputId": "54545c64-073b-41c5-d03a-cc9cbff204f2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "# Create an ANN model\n",
        "model9 = Sequential()\n",
        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "model9.add(Dense(16, activation='relu'))\n",
        "model9.add(Dense(1, activation='linear'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "ZIf94WLMlv04"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "model9.compile(loss='mean_squared_error', optimizer='adam')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX5DTKqslxWf",
        "outputId": "d2353bcd-1508-469b-e4a4-9344af25a2cc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a32a9ab7fa0>"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVW2qpNsmGVq",
        "outputId": "36e4620e-9823-4b12-d6d2-509c0bcf2a88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test set\n",
        "pred9 = model9.predict(X_test_scaled).flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "CqRmjMj2maJY"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
        "mae9 = mean_absolute_error(y_test, pred9)\n",
        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zuwkC1emmh3",
        "outputId": "3154af15-1dba-4edc-9b4e-f012f6ff6886"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 2.7525331773064208\n",
            "MAE: 1.7070852219826735\n",
            "MAPE: 0.011166669166884299\n",
            "Accuracy: 0.901060070671378\n",
            "Precision: 0.9446640316205533\n",
            "Confusion Matrix:\n",
            " [[558  42]\n",
            " [ 98 717]]\n",
            "Recall: 0.8797546012269939\n",
            "F1 Score: 0.9110546378653113\n"
          ]
        }
      ],
      "source": [
        "# Print the evaluation metrics\n",
        "print(\"RMSE:\", rmse9)\n",
        "print(\"MAE:\", mae9)\n",
        "print(\"MAPE:\", mape9)\n",
        "print(\"Accuracy:\", accuracy9)\n",
        "print(\"Precision:\", precision9)\n",
        "print(\"Confusion Matrix:\\n\", confusion9)\n",
        "print(\"Recall:\", recall9)\n",
        "print(\"F1 Score:\", f19)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjSMQNcOnFPJ"
      },
      "source": [
        "## 10. LSTM(Long Short term Memory)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "nCoyUanhnDKw"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "ThcXESVEVv0U"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Scale the features using Min-Max scaling\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "uACvajfImrbB"
      },
      "outputs": [],
      "source": [
        "# Reshape the input data for LSTM\n",
        "n_features = X_train_scaled.shape[1]\n",
        "n_steps = 10\n",
        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
        "\n",
        "# Reshape the input data\n",
        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r066pVYpnXH5",
        "outputId": "faddb972-58cc-43a7-eb04-4829ec65f019"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ],
      "source": [
        "# Create an LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
        "model.add(Dense(1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "YpSfHu6gov35"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0vHjcluaoxzP",
        "outputId": "8ca3e888-84d1-4c79-e045-cfa9d3e76d5f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a32a708e1a0>"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train the model\n",
        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEE06_TjozYv",
        "outputId": "5a39a4df-0a56-45f5-ac4b-c2b00f0f15d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step\n"
          ]
        }
      ],
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test_reshaped).flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "7k6C8DrxpB_Q"
      },
      "outputs": [],
      "source": [
        "# Calculate evaluation metrics\n",
        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_6-UUDhpi0c",
        "outputId": "af51da10-aedc-479d-80ca-96880408422c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 11.43451426022395\n",
            "MAE: 9.387460991819042\n",
            "MAPE: 0.1689158970124316\n",
            "Accuracy: 0.9957325746799431\n",
            "Precision: 0.9965277777777778\n",
            "Recall: 0.9930795847750865\n",
            "F1 Score: 0.9948006932409013\n",
            "Confusion Matrix:\n",
            " [[826   2]\n",
            " [  4 574]]\n"
          ]
        }
      ],
      "source": [
        "# Print evaluation metrics\n",
        "print(\"RMSE:\", rmse10)\n",
        "print(\"MAE:\", mae10)\n",
        "print(\"MAPE:\", mape10)\n",
        "print(\"Accuracy:\", accuracy10)\n",
        "print(\"Precision:\", precision10)\n",
        "print(\"Recall:\", recall10)\n",
        "print(\"F1 Score:\", f110)\n",
        "print(\"Confusion Matrix:\\n\", confusion10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "qpWPtph9CGip",
        "outputId": "746d4c8b-bd76-4873-d9b3-b21e078b15bd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
        "accuracies = [accuracy1*100, accuracy2*100, accuracy2_1*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy8_1*100, accuracy9*100, accuracy10*100]\n",
        "\n",
        "# List of corresponding labels for each accuracy\n",
        "labels = ['1', '2', '2_1', '3', '4', '5', '6', '7', '8', '8_1', '9', '10']\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(labels, accuracies, color='blue')\n",
        "plt.xlabel('Accuracy Variables')\n",
        "plt.ylabel('Accuracy Values')\n",
        "plt.title('Bar Graph of Accuracies')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RFaaCNH6Cfoa",
        "outputId": "e624dad4-1663-4a68-90ec-b569e5b0b46a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
        "rmse_values = [rmse1, rmse2, rmse2_1, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse8_1, rmse9, rmse10]\n",
        "\n",
        "# List of corresponding labels for each RMSE value\n",
        "labels = ['RMSE1', 'RMSE2', 'RMSE2_1', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE8_1', 'RMSE9', 'RMSE10']\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(labels, rmse_values, color='green')\n",
        "plt.xlabel('RMSE Variables')\n",
        "plt.ylabel('RMSE Values')\n",
        "plt.title('Bar Graph of RMSE')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "nrZu-K-KDCJ2",
        "outputId": "d0dd5f18-77bd-41f4-c21b-09e71322b318"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a list of MAE values from mae1 to mae10\n",
        "mae_values = [mae1, mae2, mae2_1, mae3, mae4, mae5, mae6, mae7, mae8, mae8_1, mae9, mae10]\n",
        "\n",
        "# List of corresponding labels for each MAE value\n",
        "labels = ['MAE1', 'MAE2', 'MAE2_1', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE8_1', 'MAE9', 'MAE10']\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(labels, mae_values, color='orange')\n",
        "plt.xlabel('MAE Variables')\n",
        "plt.ylabel('MAE Values')\n",
        "plt.title('Bar Graph of MAE')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "_c4Pe76fDNM-",
        "outputId": "c1784b34-6746-42fd-9f66-b7c1cfd50a21"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
        "mape_values = [mape1, mape2, mape2_1, mape3, mape4, mape5, mape6, mape7, mape8, mape8_1, mape9, mape10]\n",
        "\n",
        "# List of corresponding labels for each MAPE value\n",
        "labels = ['MAPE1', 'MAPE2', 'MAPE2_1', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE2_1', 'MAPE9', 'MAPE10']\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(labels, mape_values)\n",
        "plt.xlabel('MAPE Variables')\n",
        "plt.ylabel('MAPE Values')\n",
        "plt.title('Bar Graph of MAPE')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCF5ZehcARcw",
        "outputId": "ed3103b5-2636-48c5-82ba-60f8b3dfd729"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Length of labels: 12\n",
            "Length of mape_values: 10\n"
          ]
        }
      ],
      "source": [
        "print(\"Length of labels:\", len(labels))\n",
        "print(\"Length of mape_values:\", len(mape_values))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZDPV0M5rDTi6",
        "outputId": "2b5b43fb-f025-41ff-dc55-6193541132e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a list of precision values from precision1 to precision10\n",
        "precision_values = [precision1, precision2, precision2_1, precision3, precision4, precision5, precision6, precision7, precision8, precision8_1, precision9, precision10]\n",
        "\n",
        "# List of corresponding labels for each precision value\n",
        "labels = ['Precision1', 'Precision2', 'Precision2_1', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision8_1', 'Precision9', 'Precision10']\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(labels, precision_values, color='red')\n",
        "plt.xlabel('Precision Variables')\n",
        "plt.ylabel('Precision Values')\n",
        "plt.title('Bar Graph of Precision')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "39LBleNeDeuw",
        "outputId": "66c1cd08-96bb-46bb-afd3-7792e2b71345"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have a list of recall values from recall1 to recall10\n",
        "recall_values = [recall1, recall2, recall2_1, recall3, recall4, recall5, recall6, recall7, recall8, recall8_1, recall9, recall10]\n",
        "\n",
        "# List of corresponding labels for each recall value\n",
        "labels = ['Recall1', 'Recall2', 'Recall2_1', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall8_1', 'Recall9', 'Recall10']\n",
        "\n",
        "# Plotting the bar graph\n",
        "plt.bar(labels, recall_values, color='cyan')\n",
        "plt.xlabel('Recall Variables')\n",
        "plt.ylabel('Recall Values')\n",
        "plt.title('Bar Graph of Recall')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "13cZXvb0DsvK"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "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": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}