Prathiksha-Ramesh/Algerian-Forest-Fire-Data-Analysis

# Algerian Forest Fire Data Analysis ## Overview This project involves analyzing the Algerian forest fire dataset to understand the patterns and factors contributing to forest fires. The analysis includes data preprocessing, exploratory data analysis (EDA), visualization, and predictive modeling. ## Dataset The dataset used in this project is the 'Algerian_forest_fires_dataset_UPDATE.csv'. It contains several meteorological variables and fire weather indices (FWI) recorded daily. ### Features - **day**: Day of the month (1-31) - **month**: Month of the year (1-12) - **year**: Year (2012) - **Temperature**: Temperature in Celsius - **RH**: Relative Humidity (%) - **Ws**: Wind speed (km/h) - **Rain**: Rainfall (mm) - **FFMC**: Fine Fuel Moisture Code - **DMC**: Duff Moisture Code - **DC**: Drought Code - **ISI**: Initial Spread Index - **BUI**: Buildup Index - **FWI**: Fire Weather Index - **Classes**: Fire occurrence (Yes/No) ## Analysis The analysis is performed using Python, leveraging libraries such as pandas, numpy, matplotlib, and seaborn. The following steps outline the major components of the analysis: ### 1. Data Loading The dataset is loaded into a pandas DataFrame for processing and analysis. ### 2. Data Cleaning - Handle missing values - Correct data types - Remove any duplicate entries ### 3. Exploratory Data Analysis (EDA) - Statistical summary of the data - Visualizations to understand distributions and relationships among variables - Time series analysis of fire occurrences ### 4. Feature Engineering - Creation of new features if necessary - Scaling and normalization of data for modeling ### 5. Modeling - Implementing various machine learning models to predict fire occurrences - Model evaluation using appropriate metrics (e.g., accuracy, precision, recall) ## Results The results of the analysis, including visualizations and model performance metrics, are documented within the Jupyter notebook. …