Algerian Forest Fire Prediction

🌲 Machine Learning Project for Predicting Forest Fires in Algeria 🌲
A modern UI/UX README for your ML repo

---

🚀 Project Overview

Predicting forest fires is crucial for environmental protection and disaster management. This project leverages machine learning to predict the likelihood of forest fires in Algeria using meteorological and environmental data.

• Goal: Build, train, and deploy a robust ML model for reliable forest fire prediction.
• Tech Stack: Python, Scikit-learn, Pandas, Matplotlib, Streamlit (for UI), and more.

---

🗂️ Files Map

A quick overview of the repository structure for easy navigation:

📦 BASIC-ML-PROJECT-01-Algerian-Forest-fire-Predection
 ┣ 📁 data/
 ┃ ┗ 📄 Algerian_Forest_Fires_Dataset.csv
 ┣ 📁 notebooks/
 ┃ ┗ 📄 EDA_and_Modeling.ipynb
 ┣ 📁 src/
 ┃ ┣ 📄 data_preprocessing.py
 ┃ ┣ 📄 model_training.py
 ┃ ┣ 📄 prediction_service.py
 ┣ 📁 ui/
 ┃ ┗ 📄 app.py
 ┣ 📄 requirements.txt
 ┣ 📄 README.md
 ┗ 📄 LICENSE

---

🖥️ Demo

Try the interactive web app powered by Streamlit for real-time predictions!
(Link placeholder: Deploy on Streamlit Cloud or similar platform)

---

📊 Features

• Data Preprocessing: Clean and prepare raw data for modeling.
• Exploratory Data Analysis: Visualizations and insights.
• Model Training: Multiple ML algorithms tested for best performance.
• Prediction Service: REST or UI for forest fire risk prediction.
• Beautiful UI: User-friendly interface for non-technical users.

---

🔧 Quickstart

1. Clone the repo:

   git clone https://github.com/mdzaheerjk/BASIC-ML-PROJECT-01-Algerian-Forest-fire-Predection.git
   cd BASIC-ML-PROJECT-01-Algerian-Forest-fire-Predection

2. Install dependencies:

   pip install -r requirements.txt

3. Run the app:

   streamlit run ui/app.py

---

📈 Example Usage

(Replace with your streamlit app screenshot or gif)

---

💡 Contributing

Found a bug or have a feature idea?
Feel free to open an issue or submit a pull request!

---

📚 License

This project is licensed under the MIT License.

---

Made with ❤️ for the environment