ML_Project.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mariya347/MachineLearning/blob/main/ML_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "## **Project – 1**\n",
        "Part (a) Regression\n",
        "\n",
        "*   Bone marrow transplant: children Dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "kULFy7XAGVj4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install liac-arff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IlAfVU5pYfXc",
        "outputId": "92cec840-a1d6-4a82-90e4-c54ae5c212f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: liac-arff in /usr/local/lib/python3.10/dist-packages (2.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tabulate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WJxskkT-GSl",
        "outputId": "86965bfd-d346-468f-e300-e4604b723c46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (0.9.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwiTyHjVWZuN",
        "outputId": "a8336888-b2ce-40dc-e76f-c7247a926dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Import necessary libraries**"
      ],
      "metadata": {
        "id": "kKQyRMj7Gkzf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import arff\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tabulate import tabulate\n",
        "from sklearn.model_selection import KFold, RandomizedSearchCV\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error"
      ],
      "metadata": {
        "id": "API9i2NhZMGe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the dataset from ARFF file**"
      ],
      "metadata": {
        "id": "MhlFpuzFH5J0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_arff(file_path):\n",
        "    with open(file_path, 'r') as f:\n",
        "        dataset = arff.load(f)\n",
        "    return dataset"
      ],
      "metadata": {
        "id": "zysuMtnxZU41"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Convert ARFF data to pandas DataFrame**"
      ],
      "metadata": {
        "id": "hthC-TQRQnyr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def arff_to_dataframe(dataset):\n",
        "    data = np.array(dataset['data'])\n",
        "    attributes = [attr[0] for attr in dataset['attributes']]\n",
        "    df = pd.DataFrame(data, columns=attributes)\n",
        "    return df"
      ],
      "metadata": {
        "id": "_KwKoj0JZbC6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**File path in Google Drive**"
      ],
      "metadata": {
        "id": "elvVd8PFQwKu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/MyDrive/bone-marrow.arff'"
      ],
      "metadata": {
        "id": "duphrS3gZgTE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the dataset**"
      ],
      "metadata": {
        "id": "eV-dX9dnSKpD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_arff(file_path)\n",
        "dataFrame = arff_to_dataframe(dataset)"
      ],
      "metadata": {
        "id": "9wRgMO8wZnxN"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "dataFrame.head()"
      ],
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          "height": 256
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        "outputId": "41e068ca-871b-4ab5-b944-f6424bbd91ec"
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              "  Recipientgender Stemcellsource   Donorage Donorage35 IIIV Gendermatch  \\\n",
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              "    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-a3a4ae11-361e-4759-8272-515013decc33 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataFrame"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame.shape"
      ],
      "metadata": {
        "id": "7vNdRU8bcU_G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "457fd2f9-4377-4fc4-a5ad-3effdb387104"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(187, 37)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame.isnull().sum()"
      ],
      "metadata": {
        "id": "si3my0YQd9kT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84f3bdb4-538b-412b-c203-3738dac23cec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Recipientgender          0\n",
              "Stemcellsource           0\n",
              "Donorage                 0\n",
              "Donorage35               0\n",
              "IIIV                     0\n",
              "Gendermatch              0\n",
              "DonorABO                 0\n",
              "RecipientABO             1\n",
              "RecipientRh              2\n",
              "ABOmatch                 1\n",
              "CMVstatus               16\n",
              "DonorCMV                 2\n",
              "RecipientCMV            14\n",
              "Disease                  0\n",
              "Riskgroup                0\n",
              "Txpostrelapse            0\n",
              "Diseasegroup             0\n",
              "HLAmatch                 0\n",
              "HLAmismatch              0\n",
              "Antigen                  1\n",
              "Alel                     1\n",
              "HLAgrI                   0\n",
              "Recipientage             0\n",
              "Recipientage10           0\n",
              "Recipientageint          0\n",
              "Relapse                  0\n",
              "aGvHDIIIIV               0\n",
              "extcGvHD                31\n",
              "CD34kgx10d6              0\n",
              "CD3dCD34                 5\n",
              "CD3dkgx10d8              5\n",
              "Rbodymass                2\n",
              "ANCrecovery              0\n",
              "PLTrecovery              0\n",
              "time_to_aGvHD_III_IV     0\n",
              "survival_time            0\n",
              "survival_status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing**"
      ],
      "metadata": {
        "id": "P44WIb_pSxK4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace '?' with NaN\n",
        "dataFrame.replace('?', np.nan, inplace=True)\n",
        "\n",
        "# Convert all columns to numeric where possible, leaving others as is\n",
        "dataFrame = dataFrame.apply(pd.to_numeric, errors='ignore')\n",
        "\n",
        "# Replace NaN values with zero\n",
        "dataFrame.fillna(0, inplace=True)\n",
        "\n",
        "# Convert numerical columns to float\n",
        "for col in dataFrame.columns:\n",
        "    if dataFrame[col].dtype == 'object':\n",
        "        try:\n",
        "            dataFrame[col] = dataFrame[col].astype(float)\n",
        "        except ValueError:\n",
        "            pass\n",
        "\n",
        "# Convert categorical columns to numeric using pd.get_dummies\n",
        "dataFrame = pd.get_dummies(dataFrame, drop_first=True)\n",
        "\n",
        "# Check for any remaining NaN values (should be none)\n",
        "missing_values = dataFrame.isna().sum()\n",
        "\n",
        "# Check the shape of the dataframe after preprocessing\n",
        "print(f\"DataFrame shape after preprocessing: {dataFrame.shape}\")"
      ],
      "metadata": {
        "id": "GLx-Dh4QgLAG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a73d068-2dab-47a6-e5f7-9254fe9a0f39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame shape after preprocessing: (187, 40)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yALquymR3dtx",
        "outputId": "a6852e0c-826a-4fff-c9c7-bb2b1417e0ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recipientgender         0\n",
            "Stemcellsource          0\n",
            "Donorage                0\n",
            "Donorage35              0\n",
            "IIIV                    0\n",
            "Gendermatch             0\n",
            "DonorABO                0\n",
            "RecipientABO            0\n",
            "RecipientRh             0\n",
            "ABOmatch                0\n",
            "CMVstatus               0\n",
            "DonorCMV                0\n",
            "RecipientCMV            0\n",
            "Riskgroup               0\n",
            "Txpostrelapse           0\n",
            "Diseasegroup            0\n",
            "HLAmatch                0\n",
            "HLAmismatch             0\n",
            "Antigen                 0\n",
            "Alel                    0\n",
            "HLAgrI                  0\n",
            "Recipientage            0\n",
            "Recipientage10          0\n",
            "Recipientageint         0\n",
            "Relapse                 0\n",
            "aGvHDIIIIV              0\n",
            "extcGvHD                0\n",
            "CD34kgx10d6             0\n",
            "CD3dCD34                0\n",
            "CD3dkgx10d8             0\n",
            "Rbodymass               0\n",
            "ANCrecovery             0\n",
            "PLTrecovery             0\n",
            "time_to_aGvHD_III_IV    0\n",
            "survival_time           0\n",
            "survival_status         0\n",
            "Disease_AML             0\n",
            "Disease_chronic         0\n",
            "Disease_lymphoma        0\n",
            "Disease_nonmalignant    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract features and target\n",
        "X= dataFrame.drop(columns=['survival_time','survival_status'], axis=1)\n",
        "y = dataFrame['survival_time']"
      ],
      "metadata": {
        "id": "uvyB-w8IeEra"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " **Split the data into training and testing sets**"
      ],
      "metadata": {
        "id": "wbkLlmuDwuSD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "2QeQ9VOZeYTo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Training and Evaluation**\n",
        "\n",
        "*   **Hold-out Method**\n",
        "\n"
      ],
      "metadata": {
        "id": "JtUgVWLJwy7V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_regression(y_true, y_pred):\n",
        "\n",
        "  mse = mean_squared_error(y_true, y_pred)\n",
        "  rmse = np.sqrt(mse)\n",
        "  mae = mean_absolute_error(y_true, y_pred)\n",
        "  r2 = r2_score(y_true, y_pred)\n",
        "\n",
        "  return mse, rmse, mae, r2\n"
      ],
      "metadata": {
        "id": "5fuPbGUKOHCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the models\n",
        "models = {\n",
        "    'Linear Regression': LinearRegression(),\n",
        "    'Random Forest': RandomForestRegressor(),\n",
        "    'SVM': SVR(),\n",
        "    'Gradient Boosting': GradientBoostingRegressor()\n",
        "}\n",
        "\n",
        "# Initialize a list to store holdout results\n",
        "result_table_holdout = []\n",
        "\n",
        "# Iterate over each model\n",
        "for name, model in models.items():\n",
        "    # Split data into train and test sets\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    # Fit the model on training data\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Predict on the test data\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Evaluate regression metrics\n",
        "    mse, rmse, mae, r2 = evaluate_regression(y_test, y_pred)\n",
        "\n",
        "    # Append results rounded to 2 decimal places\n",
        "    result_table_holdout.append({\n",
        "        'Algorithm Name': name,\n",
        "        'MAE': round(mae, 2),\n",
        "        'MSE': round(mse, 2),\n",
        "        'RMSE': round(rmse, 2),\n",
        "        'r2_score': round(r2, 2)\n",
        "    })\n",
        "\n",
        "# Convert holdout results to DataFrame\n",
        "result_table_holdout = pd.DataFrame(result_table_holdout)\n",
        "\n",
        "table = tabulate(result_table_holdout, headers='keys', tablefmt='pretty')\n",
        "print(table)"
      ],
      "metadata": {
        "id": "0imP83YhObmu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2dc2356-b1fe-4137-9b10-95839024ecb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+-------------------+--------+------------+---------+----------+\n",
            "|   |  Algorithm Name   |  MAE   |    MSE     |  RMSE   | r2_score |\n",
            "+---+-------------------+--------+------------+---------+----------+\n",
            "| 0 | Linear Regression | 740.77 | 777308.02  | 881.65  |   0.16   |\n",
            "| 1 |   Random Forest   | 709.92 | 734067.67  | 856.78  |   0.2    |\n",
            "| 2 |        SVM        | 845.83 | 1204535.99 | 1097.51 |  -0.31   |\n",
            "| 3 | Gradient Boosting | 693.03 | 715640.46  | 845.96  |   0.22   |\n",
            "+---+-------------------+--------+------------+---------+----------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization**"
      ],
      "metadata": {
        "id": "XtHjLXxV_vKQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "metrics = ['MAE', 'MSE', 'RMSE', 'r2_score']\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 6))\n",
        "\n",
        "for i, metric in enumerate(metrics):\n",
        "    ax = axes[i // 2, i % 2]\n",
        "    result_table_holdout.plot(x='Algorithm Name', y=metric, kind='bar', ax=ax, width=0.5, color='darkblue', edgecolor='black')\n",
        "    ax.set_title(metric, fontsize=12, fontweight='bold')\n",
        "    ax.set_xlabel('')\n",
        "    ax.set_ylabel(metric)\n",
        "    ax.set_xticklabels(result_table_holdout['Algorithm Name'], rotation=45, ha='right', fontsize=8)\n",
        "\n",
        "plt.tight_layout(pad=2.0)\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "6E2BZItU_Q9W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "8f6de323-b39d-4a55-a4b8-31f47fcdccc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cross-validation k-fold method**"
      ],
      "metadata": {
        "id": "c0Wvr5tLxves"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the models\n",
        "models = {\n",
        "    'Linear Regression': LinearRegression(),\n",
        "    'Random Forest': RandomForestRegressor(),\n",
        "    'SVM': SVR(),\n",
        "    'Gradient Boosting': GradientBoostingRegressor()\n",
        "}\n",
        "\n",
        "# Initialize a list to store results\n",
        "result_table_kfold = []\n",
        "\n",
        "# Define the number of folds for cross-validation\n",
        "num_folds = 5\n",
        "kf = KFold(n_splits=num_folds, shuffle=True, random_state=42)\n",
        "\n",
        "# Define a function to evaluate regression metrics including MDA\n",
        "def evaluate_regression(y_true, y_pred):\n",
        "    mse = mean_squared_error(y_true, y_pred)\n",
        "    rmse = np.sqrt(mse)\n",
        "    mae = mean_absolute_error(y_true, y_pred)\n",
        "    r2 = r2_score(y_true, y_pred)\n",
        "    mape = mean_absolute_percentage_error(y_true, y_pred) * 100  # Percentage error\n",
        "\n",
        "    # Mean Directional Accuracy (MDA)\n",
        "    mda = np.mean(np.sign(y_true - y_pred) == np.sign(y_true - np.mean(y_true))) * 100\n",
        "\n",
        "    return mse, rmse, mae, r2, mape, mda\n",
        "\n",
        "# Perform k-fold cross-validation for each model\n",
        "for name, model in models.items():\n",
        "    mse_scores = []\n",
        "    rmse_scores = []\n",
        "    mae_scores = []\n",
        "    r2_scores = []\n",
        "    mape_scores = []\n",
        "    mda_scores = []\n",
        "\n",
        "    for train_index, test_index in kf.split(X):\n",
        "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "        model.fit(X_train, y_train)\n",
        "        y_pred = model.predict(X_test)\n",
        "\n",
        "        # Evaluate metrics\n",
        "        mse, rmse, mae, r2, mape, mda = evaluate_regression(y_test, y_pred)\n",
        "\n",
        "        # Store scores for each fold\n",
        "        mse_scores.append(mse)\n",
        "        rmse_scores.append(rmse)\n",
        "        mae_scores.append(mae)\n",
        "        r2_scores.append(r2)\n",
        "        mape_scores.append(mape)\n",
        "        mda_scores.append(mda)\n",
        "\n",
        "    # Compute mean scores across folds\n",
        "    mean_mse = np.mean(mse_scores)\n",
        "    mean_rmse = np.mean(rmse_scores)\n",
        "    mean_mae = np.mean(mae_scores)\n",
        "    mean_r2 = np.mean(r2_scores)\n",
        "    mean_mape = np.mean(mape_scores)\n",
        "    mean_mda = np.mean(mda_scores)\n",
        "\n",
        "    # Append results to the result_table_kfold\n",
        "    result_table_kfold.append({\n",
        "        'Algorithm Name': name,\n",
        "        'Mean MAE': round(mean_mae, 2),\n",
        "        'Mean MSE': round(mean_mse, 2),\n",
        "        'Mean RMSE': round(mean_rmse, 2),\n",
        "        'Mean r2_score': round(mean_r2, 2),\n",
        "        'Mean MAPE': round(mean_mape, 2),\n",
        "        'Mean MDA': round(mean_mda, 2)\n",
        "    })\n",
        "\n",
        "# Convert result_table_kfold to DataFrame\n",
        "result_table_kfold = pd.DataFrame(result_table_kfold)\n",
        "\n",
        "table = tabulate(result_table_kfold, headers='keys', tablefmt='pretty')\n",
        "print(table)"
      ],
      "metadata": {
        "id": "b18nhPJs_RSi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c08b6e8-b3bc-40b0-8af8-e7d2511264c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+-------------------+----------+-----------+-----------+---------------+-----------+----------+\n",
            "|   |  Algorithm Name   | Mean MAE | Mean MSE  | Mean RMSE | Mean r2_score | Mean MAPE | Mean MDA |\n",
            "+---+-------------------+----------+-----------+-----------+---------------+-----------+----------+\n",
            "| 0 | Linear Regression |  628.59  | 608998.94 |   777.2   |      0.1      |  393.32   |  80.75   |\n",
            "| 1 |   Random Forest   |  636.67  | 631909.43 |  791.71   |     0.05      |  373.74   |  89.29   |\n",
            "| 2 |        SVM        |  711.28  | 803512.47 |  883.13   |     -0.13     |  442.43   |  92.01   |\n",
            "| 3 | Gradient Boosting |  657.8   | 713353.52 |  839.89   |     -0.09     |  371.39   |  86.59   |\n",
            "+---+-------------------+----------+-----------+-----------+---------------+-----------+----------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = ['Mean MAE', 'Mean MSE', 'Mean RMSE', 'Mean r2_score', 'Mean MAPE', 'Mean MDA']\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))\n",
        "\n",
        "for i, metric in enumerate(metrics):\n",
        "    ax = axes[i // 3, i % 3]\n",
        "    result_table_kfold.plot(x='Algorithm Name', y=metric, kind='bar', ax=ax, width=0.5, color='darkblue', edgecolor='black')\n",
        "    ax.set_title(metric, fontsize=12, fontweight='bold')\n",
        "    ax.set_xlabel('')\n",
        "    ax.set_ylabel(metric)\n",
        "    ax.set_xticklabels(result_table_kfold['Algorithm Name'], rotation=45, ha='right', fontsize=8)\n",
        "\n",
        "plt.tight_layout(pad=2.0)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "_pG03wk5__BV",
        "outputId": "4ebbd5dd-a340-4184-c344-b0a001547061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cross-validation k-fold method and the algorithm run on the best parameter (hyper-parameter).**"
      ],
      "metadata": {
        "id": "g8iAdvYzpynw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function for k-fold cross-validation with random search\n",
        "def kfold_cross_validation(model, X, y, hyperparameters, n_splits=5):\n",
        "    kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
        "    results = []\n",
        "\n",
        "    for train_index, val_index in kf.split(X):\n",
        "        X_train, X_val = X[train_index], X[val_index]\n",
        "        y_train, y_val = y[train_index], y[val_index]\n",
        "\n",
        "        # Setup random search cross-validation\n",
        "        random_search = RandomizedSearchCV(estimator=model, param_distributions=hyperparameters,\n",
        "                                           n_iter=10, scoring='neg_mean_squared_error',\n",
        "                                           cv=3, verbose=1, random_state=42)\n",
        "\n",
        "        # Fit random search\n",
        "        random_search.fit(X_train, y_train)\n",
        "\n",
        "        # Predict on validation set\n",
        "        y_pred = random_search.best_estimator_.predict(X_val)\n",
        "\n",
        "        # Calculate evaluation metrics\n",
        "        mse = mean_squared_error(y_val, y_pred)\n",
        "        rmse = np.sqrt(mse)\n",
        "        mae = mean_absolute_error(y_val, y_pred)\n",
        "        r2 = r2_score(y_val, y_pred)\n",
        "\n",
        "        # Calculate MAPE (Mean Absolute Percentage Error)\n",
        "        mape = np.mean(np.abs((y_val - y_pred) / y_val)) * 100\n",
        "\n",
        "        # Calculate MDA (Mean Directional Accuracy)\n",
        "        mda = 100 * np.sum((y_pred[1:] - y_pred[:-1]) * (y_val[1:] - y_val[:-1]) > 0) / len(y_pred)\n",
        "\n",
        "        results.append({\n",
        "            'MSE': mse,\n",
        "            'RMSE': rmse,\n",
        "            'MAE': mae,\n",
        "            'R2': r2,\n",
        "            'MAPE': mape,\n",
        "            'MDA': mda\n",
        "        })\n",
        "\n",
        "    # Calculate average metrics across all folds\n",
        "    avg_results = {\n",
        "        'MSE': np.mean([result['MSE'] for result in results]),\n",
        "        'RMSE': np.mean([result['RMSE'] for result in results]),\n",
        "        'MAE': np.mean([result['MAE'] for result in results]),\n",
        "        'R2': np.mean([result['R2'] for result in results]),\n",
        "        'MAPE': np.mean([result['MAPE'] for result in results]),\n",
        "        'MDA': np.mean([result['MDA'] for result in results])\n",
        "    }\n",
        "\n",
        "    return avg_results\n",
        "\n",
        "# Example data (replace with your data)\n",
        "X = np.random.rand(100, 10)  # Example feature matrix\n",
        "y = np.random.rand(100)     # Example target variable\n",
        "\n",
        "# Define hyperparameters for each algorithm\n",
        "linear_params = {}  # No hyperparameters for Linear Regression\n",
        "rf_params = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [None, 10, 20],\n",
        "    'min_samples_split': [2, 5, 10]\n",
        "}\n",
        "svm_params = {\n",
        "    'kernel': ['linear', 'rbf', 'poly'],\n",
        "    'C': np.logspace(-3, 2, 6),\n",
        "    'gamma': ['scale', 'auto']\n",
        "}\n",
        "gb_params = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [3, 5, 10],\n",
        "    'learning_rate': [0.01, 0.1, 0.5]\n",
        "}\n",
        "\n",
        "# Initialize models\n",
        "models = {\n",
        "    'Linear Regression': LinearRegression(),\n",
        "    'Random Forest': RandomForestRegressor(),\n",
        "    'SVM': SVR(),\n",
        "    'Gradient Boosting': GradientBoostingRegressor()\n",
        "}\n",
        "\n",
        "# Perform k-fold cross-validation for each algorithm and store results\n",
        "results = []\n",
        "for name, model in models.items():\n",
        "    if name == 'Linear Regression':\n",
        "        hyperparams = linear_params\n",
        "    elif name == 'Random Forest':\n",
        "        hyperparams = rf_params\n",
        "    elif name == 'SVM':\n",
        "        hyperparams = svm_params\n",
        "    elif name == 'Gradient Boosting':\n",
        "        hyperparams = gb_params\n",
        "\n",
        "    avg_results = kfold_cross_validation(model, X, y, hyperparams)\n",
        "    avg_results['Algorithm Name'] = name\n",
        "    results.append(avg_results)\n",
        "\n",
        "# Convert results to DataFrame\n",
        "result_df = pd.DataFrame(results)"
      ],
      "metadata": {
        "id": "gQLMsxI0XJf2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reorder columns as per the table format\n",
        "result_df = result_df[['Algorithm Name', 'MAE', 'MSE', 'RMSE', 'R2', 'MAPE', 'MDA']]\n",
        "\n",
        "# Add a column for Sno (Serial Number)\n",
        "result_df.insert(0, 'Sno', range(1, len(result_df) + 1))\n",
        "\n",
        "# Print the final table using tabulate\n",
        "print(\"Table-3: Performance Metrics of Different Algorithms using k-fold Cross-Validation and Random Search\")\n",
        "print(tabulate(result_df, headers='keys', tablefmt='psql', showindex=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzYIOWg9XqPs",
        "outputId": "166ef2b0-fc40-4e27-d073-388cec70aaf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Table-3: Performance Metrics of Different Algorithms using k-fold Cross-Validation and Random Search\n",
            "+-------+-------------------+----------+-----------+----------+-----------+---------+-------+\n",
            "|   Sno | Algorithm Name    |      MAE |       MSE |     RMSE |        R2 |    MAPE |   MDA |\n",
            "|-------+-------------------+----------+-----------+----------+-----------+---------+-------|\n",
            "|     1 | Linear Regression | 0.25723  | 0.086564  | 0.292263 | -0.211202 | 144.833 |    55 |\n",
            "|     2 | Random Forest     | 0.253961 | 0.0888207 | 0.296242 | -0.245992 | 148.225 |    52 |\n",
            "|     3 | SVM               | 0.248658 | 0.0842868 | 0.288212 | -0.18019  | 162.915 |    51 |\n",
            "|     4 | Gradient Boosting | 0.247993 | 0.0819664 | 0.284936 | -0.154519 | 152.323 |    47 |\n",
            "+-------+-------------------+----------+-----------+----------+-----------+---------+-------+\n"
          ]
        }
      ]
    }
  ]
}