GST Hackathon Hybrid Model

Description

This project implements a hybrid machine learning model using Support Vector Machine (SVM) and Gradient Boosting for predicting GST outcomes based on a provided dataset. The dataset consists of training and testing matrices, and the goal is to construct a predictive model that accurately estimates the target variable for new, unseen inputs.

Problem Statement

Given a dataset (D), the objective is to construct a predictive model (F_\theta(X) \rightarrow Y_{\text{pred}}) that accurately estimates the target variable (Y_i) for new, unseen inputs (X_i).

Dataset Description

• Xtrain: A matrix of dimension (R(m \times n)) representing the training data.
• Xtest: A matrix of dimension (R(m1 \times n)) representing the test data.
• Ytrain: Corresponding target variable with matrix dimension (R(m \times 1)).
• Ytest: Corresponding target variable with matrix dimension (R(m1 \times 1)).

Features

• Hybrid model combining SVM and Gradient Boosting.
• Data preprocessing including feature scaling and label encoding.
• Prediction script to generate results based on new input data.

Tech Stack

• Programming Language: Python
• Libraries:
  • scikit-learn: For machine learning algorithms and data preprocessing.
  • pandas: For data manipulation and analysis.
  • joblib: For model serialization (saving and loading).
  • numpy: For numerical operations and array handling.
• stacks:
  • 
• Development Environment:
  • Visual Studio Code: For coding and debugging.
• Version Control: Git

Steps to Execute

To run this project, ensure you have Python installed on your machine. Follow the steps below to set up the environment:

1. Clone this repository:

   git clone https://github.com/yourusername/GSTAnalytics.git
   cd repository-name
   

2. Create a virtual environment

   python -m venv venv
   

3. Activate the virtual environment:
   • On Windows:

     venv\Scripts\activate
     

   • On macOS/Linus:

     source venv/bin/activate
     

4. Install the required packages:

   pip install -r requirements.txt
   

Usage

1. Prepare your input data in a CSV file named input_data.csv

2. Run the prediction script:

   python predict.py
   

3. View the predictions in the console output.

Project Structure

• /project-root
  • ├── model.py # Script for training the model
  • ├── predict.py # Script for making predictions
  • ├── input_data.csv # Sample input data for predictions
  • ├── requirements.txt # Required Python packages
  • ├── .gitignore # Files and directories to ignore in git
  • └── README.md # Project documentation

Checksum Value

- F:\>python checksum.py "GSTAnalytics.zip" --algorithm sha256
SHA256 Checksum: a436d8f6e7cb0947e0918aff7b0906b0f4a6cfe4e9f83252af4cefaf9631814c