This project encapsulates the full lifecycle of a data science initiative, from the initial problem formulation through to the deployment of a machine learning model into production. The goal is to provide actionable insights or predictive capabilities based on data analysis and modeling.
The project is structured to follow best practices in data science and software development, ensuring reproducibility, scalability, and ease of maintenance. It includes the following components:
- Data Ingestion: Automated scripts to fetch or generate the project's data.
- Data Transformation: A pipeline that prepares the data for analysis by cleaning and structuring it.
- Exploratory Data Analysis (EDA): Jupyter notebooks or scripts that explore the data to find patterns, relationships, anomalies, etc.
- Feature Engineering: Code to transform raw data into features suitable for model training.
- Model Training: Scripts to train machine learning models.
- Model Evaluation: Evaluation of model performance with various metrics.
- Hyperparameter Tuning: Optimization of model parameters for the best performance.
- Deployment: Deployment of the model into a production environment using Flask for creating a web application and GitHub Actions for CI/CD workflows in Azure Cloud.
- Pipelines: For automating data processing and model training workflows.
- Exception Handling and Logging: Custom modules for managing exceptions and logging throughout the project lifecycle.
- Testing: Unit tests and integration tests for code reliability and stability.
This project was inspired by and has utilized concepts taught by krishnaik-github. His youtube-playlist provided invaluable insights and guidance.
This project is open source and available under the MIT License. It is intended for educational purposes and personal skill showcasing.