darkknight3097/forest-fire-classification

Predicting forest fire conditions using SVM and Random Forest classifiers. 96% accuracy on the Algerian Forest Fire dataset. # Forest Fire Classification Model Binary classification model to predict fire vs non-fire conditions using the Algerian Forest Fire dataset. ## Overview Built and evaluated two machine learning classifiers — Support Vector Machine (SVM) and Random Forest — to predict whether weather and environmental conditions would result in a forest fire. ## Results | Metric | Score | |--------|-------| | Accuracy | 96% | | Precision | 96% | | Recall | 96% | ## Tech Stack - Python - scikit-learn (SVM, Random Forest) - Pandas - Google Colab ## Dataset Algerian Forest Fire Dataset — UCI Machine Learning Repository. Contains meteorological data from two regions of Algeria (Bejaia and Sidi Bel-abbes) collected June–September 2012. ## Key Steps - Data loading and exploratory data analysis (EDA) - Data preprocessing and feature engineering - Model training — SVM and Random Forest classifiers - Model evaluation — accuracy, precision, recall - Comparison of model performance ## How to Run Open the notebook in Google Colab or Jupyter and run all cells.