Saikiranpoluka/Algerian-Forest-Fire-Prediction

# 🌲 Algerian Forest Fire Prediction > An end-to-end machine-learning project for predicting forest-fire occurrence from weather and environmental measurements, with a Flask inference application and deployment configuration. ## Overview The project demonstrates a complete ML workflow: exploratory analysis, preprocessing, feature engineering, model training, evaluation, and model serving. ## Architecture ```text Weather / Environmental Data ↓ EDA & Data Validation ↓ Feature Engineering / Scaling ↓ Model Training & Evaluation ↓ Serialized Model ↓ Flask Inference API / Web UI ↓ Docker Deployment ``` ## Project Structure ```text Algerian-Forest-Fire-Prediction/ ├── end_to_end_project_implimentation/ ├── flask/ ├── Dockerfile ├── Jenkinsfile ├── .gitignore └── README.md ``` ## 🛠️ Tech Stack - Python - Pandas / NumPy - Scikit-learn - Jupyter Notebook - Flask - Docker - Jenkins ## 🚀 Local Setup Review the application-specific requirements in the repository, install the required Python packages, then run the Flask application from the `flask/` directory. For container deployment, verify the Dockerfile locally before publishing deployment claims. ## 🔬 Engineering Highlights - Covers the full path from data preparation to inference. - Separates notebook experimentation from the web-serving layer. - Includes Docker configuration for reproducible packaging. - Includes Jenkins configuration as a starting point for CI/CD automation. ## ⚠️ Evaluation & Deployment Note The README previously described the system as "production-ready" and claimed automated CI/CD deployment. Those statements should only be used if the model evaluation, deployment environment, Jenkins pipeline, tests, and monitoring have been verified end-to-end. This repository is therefore positioned as a **deployment-oriented ML project**, not as a claim of production operation. ## 🎯 Portfolio Position This is a useful supporting project for demonstrating **ML deployment, Flask, Docker, and CI/CD …