ML-Project is an end-to-end machine learning project for regression tasks. It includes modules for data ingestion, preprocessing, exploratory data analysis (EDA), feature transformation, model training, and prediction pipelines. This project is structured to be modular, maintainable, and scalable for real-world ML applications.
- Data Ingestion: Load datasets from local files or remote sources.
- Data Preprocessing: Handle missing values, encode categorical variables, scale numerical features, and clean data.
- Exploratory Data Analysis (EDA): Analyze data distributions, correlations, and outliers.
- Feature Transformation: Apply transformations and feature engineering to improve model performance.
- Model Training: Train regression models, evaluate performance, and save the best model.
- Prediction Pipeline: Perform end-to-end predictions on new data using the trained model.
- Logging & Exception Handling: Comprehensive logging and custom exception handling for easier debugging and maintenance.
- Clone the repository:
git clone https://github.com/Immanuel2004/Ml-Project2.git
cd ML-Project2python -m venv venv
source venv/bin/activate # On macOS/Linux
venv\Scripts\activate # On Windowspip install -r requirements.txtpython main.py