humayunattar/Algerian_forest_fire_prediction

# 🔥 Algerian Forest Fires Prediction ### 🚀 End-to-End ML Project | Streamlit Deployment | Real-World Use Case --- ## 🧠 Problem Statement Forest fires are unpredictable, destructive, and costly. This project solves a **real-world problem**: 👉 Predicting fire risk using meteorological data before it happens. --- ## ⚡ What Makes This Project Stand Out * 🔥 Full **end-to-end ML pipeline** * 📊 Real-world dataset (Algerian Forest Fires) * 🧪 Proper **EDA + Feature Engineering** * ⚙️ Model optimization (Ridge Regression) * 🌐 Interactive **Streamlit Web App** * 📦 Clean, production-ready structure --- ## 🎯 Live Demo (Add This ASAP) > 🚨 If this is missing, your project loses 70% impact. 👉 streamlit-app-link --- ## 📊 Dataset Overview The dataset includes environmental and weather conditions: | Feature | Description | | ------------------ | ----------------------------- | | Temperature | Atmospheric temperature | | RH | Relative Humidity | | Ws | Wind Speed | | Rain | Rainfall | | FFMC, DMC, DC, ISI | Fire Weather Index components | | Region | Bejaia / Sidi Bel-Abbes | --- ## 🧩 ML Pipeline ```mermaid graph TD; A[Raw Data] --> B[Data Cleaning] B --> C[EDA] C --> D[Feature Engineering] D --> E[Scaling] E --> F[Model Training] F --> G[Evaluation] G --> H[Deployment] ``` --- ## ⚙️ Tech Stack * **Python** * **NumPy, Pandas** * **Scikit-learn** * **Matplotlib / Seaborn** * **Streamlit** --- ## 🧠 Model Performance | Metric | Value | | ---------- | ---------------- | | R² Score | High (Good Fit) | | RMSE | Low | | Model Used | Ridge Regression | --- ## 🚀 Run Locally ```bash git clone github.com cd Algerian_Forest_Fires_Prediction pip install -r requirements.txt streamlit run app.py ``` --- ## 📂 Project …