ishankeshre/Algerian-forest-fires-analysis

🔥 Algerian Forest Fires – Data Analysis Project This project explores and analyzes the **Algerian Forest Fires Dataset**, focusing on identifying patterns and potential indicators of forest fires based on meteorological and environmental conditions. 📂 Dataset Overview - **File:** `Algerian_forest_fires_dataset_UPDATE.csv` - **Source:** UCI Machine Learning Repository - **Regions Covered:** Bejaia Region and Sidi-Bel Abbes Region, Algeria - **Features Include:** - Temperature, Humidity, Wind, Rain - Fire Weather Index (FWI) components: FFMC, DMC, DC, ISI - Date, Month, Day - **Target Variable:** `Classes` (Fire / Not Fire) 📊 Objectives - Perform Exploratory Data Analysis (EDA) - Understand the key features that contribute to forest fires - Build predictive models (optional): - Logistic Regression - Decision Trees - Random Forest or SVM - Visualize the correlations and patterns in the dataset 🔄 Workflow 1. **Data Cleaning** - Remove null values - Standardize feature formats - Encode target variable if needed 2. **EDA** - Summary statistics - Correlation matrix - Feature distributions (with Seaborn/Matplotlib) 3. **Modeling (Optional)** - Train/Test split - Classification model training - Evaluation: Accuracy, Precision, Recall 🧰 Tools & Libraries - Python - Pandas, NumPy - Matplotlib, Seaborn - Scikit-learn (for modeling) 📌 Insights - Explore which weather features most strongly correlate with fire outbreaks - Regional differences in fire occurrence - Seasonal and monthly trends