04_Classification_using_LightGBM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Predictions using LightGBM"
      ],
      "metadata": {
        "id": "WJIUNw0ftbrb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Loading and Preprocessing"
      ],
      "metadata": {
        "id": "Q4ELVhfKyBlF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "data = pd.read_csv('/content/data_with_predictions.csv')\n",
        "\n",
        "# Check for missing values and clean data\n",
        "print(\"Missing values:\\n\", data.isnull().sum())\n",
        "data.dropna(inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7fmeidyxb7y",
        "outputId": "0011eba9-5344-4936-92c6-ed570baf6452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values:\n",
            " date                 0\n",
            "Open                 0\n",
            "High                 0\n",
            "Low                  0\n",
            "Close                0\n",
            "Shares Traded        0\n",
            "Turnover (â¹ Cr)    0\n",
            "news                 0\n",
            "Sentiment            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Selection and Target Definition"
      ],
      "metadata": {
        "id": "v-uWB8fiymAJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The features used for the prediction are:\n",
        "* Sentiment (sentiment prediction from earlier tasks).\n",
        "* Stock trading indicators like Shares Traded, Turnover, Open, High, and Low.\n",
        "2. The target variable (Target) is defined as a binary classification problem:\n",
        "* 1: Closing price increases the next day (Close.shift(-1) > Close).\n",
        "* 0: Closing price decreases or remains the same."
      ],
      "metadata": {
        "id": "bKs2flpwzQpl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['Sentiment', 'Shares Traded ', 'Turnover (â¹ Cr)', 'Open ', 'High ', 'Low ']\n",
        "target = 'Close '\n",
        "\n",
        "data['Target'] = np.where(data['Close '].shift(-1) > data['Close '], 1, 0)\n",
        "\n",
        "X = data[features]\n",
        "y = data['Target']\n",
        "\n",
        "# Split 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, stratify=y)\n"
      ],
      "metadata": {
        "id": "3UjGTrw5yf-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LightGBM Model Initialization and Training"
      ],
      "metadata": {
        "id": "ElDIkn4YzoA0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the LightGBM model\n",
        "model = LGBMClassifier(is_unbalance=True, random_state=42, n_estimators=200, learning_rate=0.1)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_proba = model.predict_proba(X_test)[:, 1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zsu1q1q4xgQy",
        "outputId": "0f84892f-148f-4f87-d472-0fd0e72fd6b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
            "[LightGBM] [Info] Number of positive: 242, number of negative: 16102\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001267 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 1270\n",
            "[LightGBM] [Info] Number of data points in the train set: 16344, number of used features: 6\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.014807 -> initscore=-4.197761\n",
            "[LightGBM] [Info] Start training from score -4.197761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "WgCBBOnHzw_2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Confusion Matrix:\")\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(conf_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqQD4JOzxk-3",
        "outputId": "84359045-8884-40a5-d0b6-9aebbe3adc2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[2956 1070]\n",
            " [  60    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"\\nAccuracy: {accuracy:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqDP7SP5xnt6",
        "outputId": "9aae2a83-d216-4f96-d712-eadbed2c08b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.73      0.84      4026\n",
            "           1       0.00      0.00      0.00        60\n",
            "\n",
            "    accuracy                           0.72      4086\n",
            "   macro avg       0.49      0.37      0.42      4086\n",
            "weighted avg       0.97      0.72      0.83      4086\n",
            "\n",
            "\n",
            "Accuracy: 0.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualize Confusion Matrix"
      ],
      "metadata": {
        "id": "BQZ4-hIIz3iv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Decrease', 'Increase'], yticklabels=['Decrease', 'Increase'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "PZRTYHMPxqX2",
        "outputId": "7a3f79c2-7461-4d86-d498-ff984cfb3cf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Importance Visualization"
      ],
      "metadata": {
        "id": "HfxjSdDiz8DZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importances = model.feature_importances_\n",
        "sorted_indices = np.argsort(feature_importances)[::-1]\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=feature_importances[sorted_indices], y=np.array(features)[sorted_indices], palette='viridis')\n",
        "plt.title('Feature Importance')\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Feature')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "b2apDKLaxvbr",
        "outputId": "2f4f860d-df58-463e-c67b-dafbe3bd3ac3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-16909cab22b1>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=feature_importances[sorted_indices], y=np.array(features)[sorted_indices], palette='viridis')\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 130 (\\x82) missing from current font.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}