rajabharathi1001-hue/EDA-and-Feature-Engineering-on-Algerian-Forest-Fire-Dataset

Applied EDA and Feature Engineering on the Algerian Forest Fire dataset to clean data, visualize monthly fire trends, compare regions (Sidi‑Bel Abbes & Brijiya), and study fire class distributions. Highlights seasonal patterns and prepares features for predictive modeling. # EDA-and-Feature-Engineering-on-Algerian-Forest-Fire-Dataset Applied EDA and Feature Engineering on the Algerian Forest Fire dataset to clean data, visualize monthly fire trends, compare regions (Sidi‑Bel Abbes & Brijiya), and study fire class distributions. Highlights seasonal patterns and prepares features for predictive modeling. This project explores the Algerian Forest Fire dataset through Exploratory Data Analysis (EDA) and Feature Engineering (FE). The aim is to uncover seasonal fire patterns, compare regions, and prepare the dataset for predictive modeling. 🛠️ Workflow Data Cleaning: Handle missing values, duplicates, and inconsistent formats. EDA: Visualize monthly fire counts, regional differences, and class distributions. Feature Engineering: Create new variables to capture seasonal trends and fire behavior. Insights: Identify peak fire months, compare Sidi‑Bel Abbes vs. Brijiya regions, and highlight fire class proportions. 📊 Tools & Libraries Python pandas, NumPy seaborn, matplotlib scikit‑learn (for preprocessing and feature creation) 📈 Results Clear visualization of fire incidents by month and class. Regional comparison showing distinct seasonal fire activity. Recruiter‑ready plots (bar charts, pie charts, KDE distributions). Feature set prepared for downstream modeling tasks.