Harish2873/Algerian-Forest-Fire-Using-Regression

# 🔥 Algerian Forest Fire Prediction using Regression ## About Dataset The dataset includes 244 instances that regroup a data of two regions of Algeria, namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria. 122 instances for each region. - The period from June 2012 to September 2012. - The dataset includes 11 attributes and 1 output attribute (class) - The 244 instances have been classified into fire (138 classes) and not fire (106 classes) classes. ## Why Use FWI as the Target? 1. Holistic Fire Risk Measurement It combines weather conditions—temperature, humidity, wind, precipitation—and fuel moisture into a single numeric index representing fire intensity and spread potential 2. Continuous Regression Value - Provides more nuance than a binary “fire/no-fire” classification. - Enables modeling of fire intensity rather than just presence. 3. Strong Correlation with Fire Events - FWI serves as a better proxy for actual fire occurrence since it correlates highly with the binary Classes label - As seen in similar projects, predicting FWI offers deeper insight into fire dynamics and stronger regression performance. 4. Interpretable & Actionable - Fire management authorities frequently use FWI for early warning and resource allocation. - Predicting its value helps assess real-world fire risk. --- ## 📈 Data Exploration and Visualizations ### 📦 Boxplot Before and After Standard Scaling ### 📊 Distribution of Numerical Features (Histogram) ### 🔥 Correlation Heatmap ### 📉 Actual vs Predicted Values Plot ### 🔥🔥 Fire vs No-Fire Pie Chart --- ## Streamlit 🔗 Live Demo 👉 Click here to open the Streamlit App ---