Haarit2966/Algerian-Forest-Fire

This is a machine learning project for predicting forest fire risks in Algeria using Python. It analyzes weather and environmental data to identify fire patterns and forecast possible fire occurrences. The project includes data preprocessing, visualization, and predictive models, demonstrating practical data science and machine learning concepts. # Algerian Forest Fire Prediction (Machine Learning Project) The **Algerian Forest Fire Prediction** is a comprehensive machine learning project written in **Python using Jupyter Notebook**. It provides a complete data analysis and predictive modeling pipeline where users can **analyze, preprocess, visualize, and predict forest fire occurrences** based on weather and environmental data. The project is designed to demonstrate the use of **data preprocessing, exploratory data analysis (EDA), machine learning algorithms, and data visualization** while showing how a real-world problem like forest fire prediction can be solved using core data science concepts. --- ## Project Overview Forest fires are a major environmental concern in Algeria, causing significant damage to ecosystems and communities. Handling fire prediction manually or reactively can be ineffective and costly. This project is a **data-driven solution** to predict and understand forest fire occurrences efficiently. The program analyzes details such as: - Weather conditions (Temperature, Humidity, Wind Speed, Rainfall) - Fire Weather Index (FWI) components (FFMC, DMC, DC, ISI, BUI, FWI) - Temporal information (Day, Month, Year) - Fire occurrence status (Fire / Not Fire) Data is loaded from CSV files, processed and cleaned, then used to train machine learning models that can predict whether a forest fire will occur based on given environmental conditions. --- ## Features This project comes with a wide range of useful analysis and modeling capabilities: 1. **Data Loading and Exploration** - Load datasets and understand their structure and characteristics. 2. **Data Cleaning** - Handle missing values, outliers, and inconsistent data entries. 3. **Exploratory Data Analysis (EDA)** - Generate statistics and visualizations to understand data patterns. 4. **Data Visualization** - Create plots and charts showing relationships between features and the target variable. 5. **Feature Engineering** - Transform …