End-to-end Cancer Diagnosis using Machine Learning and Flask

Overview

A complete MLOps pipeline for cancer diagnosis that demonstrates data ingestion, transformation, model training & evaluation, and a Flask-based web UI for inference.

Dataset: This project uses the Cancer Prediction Dataset from Kaggle.

Features

• Reproducible Pipeline: Modular scripts for ingestion, transformation, and training.
• Robustness: Custom exception handling and logging for every step.
• Web Interface: Clean Flask UI for real-time predictions.
• Artifact Management: Systematically saves preprocessors and models for deployment.
• Notebook Experiments: Comprehensive EDA and model experiments.
• AWS Deployment-ready: Elastic beanstalk configuration file set up.

Preview (Flask Web UI)

Web.UI.demo.mp4

TODO

• Add step-by-step instructions for Elastic beanstalk deployment to README.

How to run this project

Prerequisites:

• conda
• Git

Run:

# 1. Clone repository
git clone https://github.com/mrkomoruyi/Cancer-Diagnosis-MLOps-Pipeline.git
cd Cancer-Diagnosis-MLOps-Pipeline

# 2. Create env and install
conda create -p venv python==3.12 -y
conda activate venv/
pip install -r requirements.txt

# 3. Run the training script
python src/pipeline/train_pipeline.py

# 4. Run the web app
python application.py

5. Open http://localhost:5000/predict in your browser.

Project structure (high level)

• notebook/: contains the raw dataset, exploratory data analysis (EDA), and experimentation notebooks.
• artifacts/: stores generated outputs such as raw/processed data files, the trained model.pkl, and preprocessor objects.
• src/: the core source code for the project:
  • components/: modular scripts for Data Ingestion, Transformation, and Model Training.
  • pipeline/: orchestration scripts for the Training and Prediction pipelines.
  • utils.py, logger.py, exception.py: common utility functions, custom logging, and exception handling logic.
• templates/: HTML files (index.html, home.html) for the Flask web interface.
• application.py: the main entry point for the Flask web application.
• setup.py & requirements.txt: configuration for project dependencies and package installation.

Contributing

If you find a bug, please submit an issue using the Issues tab.

If you want to submit a Pull Request, open an issue first and reference the issue in the pull request.

License

Distributed under the MIT License. See LICENSE for more information.