In this project, I developed a machine learning model to predict the marks of a subject based on the marks obtained in other subjects. The project follows a standardized format and includes several key components:
Data Pipelines: Designed and implemented data pipelines for efficient data handling and processing.
Data Ingestion: Automated the process of gathering and importing data from various sources.
Data Transformation: Applied necessary transformations to the data to ensure it was in the correct format for modeling.
Model Training: Developed and trained the machine learning model using appropriate algorithms and techniques.
Model Serialization: Serialized the trained model using pickle for easy deployment and reuse.
Flask Integration: Created a Flask application to serve the model and provide an interface for users to make predictions.
Virtual Environment: Set up a virtual environment to manage dependencies and ensure a consistent development setup.
By following this structured approach, I ensured that the project is well-organized, modular, and easy to maintain.
This is Project UI:
