ishu-005/Algerian-Forest-Fire-Analysis

Algerian Forest Fire Prediction — A machine learning project that analyzes weather data to predict forest fire risks in Algeria. Includes data cleaning, visualization, and model training for accurate fire risk assessment. # 🔥 Algerian Forest Fire Prediction Project This project analyzes and predicts forest fire occurrences in Algeria using machine learning. It explores weather parameters like temperature, humidity, wind speed, and rainfall to identify conditions leading to fires. ## 📊 Dataset - **Original Dataset:** `Algerian_forest_fires_dataset_UPDATE.csv` - **Cleaned Dataset:** `Algerian_forest_fires_cleaned_dataset.csv` - Source: UCI Machine Learning Repository - Algerian Forest Fires Dataset ## 🧠 Project Notebooks 1. **`algerianforestfires.ipynb`** – Data cleaning, preprocessing, and visualization. 2. **`modelTraning.ipynb`** – Model training using machine learning algorithms (e.g., Random Forest, Logistic Regression, etc.) ## ⚙️ Features - Data preprocessing and cleaning - Exploratory Data Analysis (EDA) - Model training and performance evaluation - Visualization of fire risk factors ## 📈 Results - The models effectively predict forest fire risk levels. - Key influencing features: temperature, relative humidity, and wind speed.