nikhilmalgar/Algerian_ForestFire_Analysis

# Algerian Forest Fire Analysis This repository contains work on cleaning, exploring, and analyzing the **Algerian Forest Fires** dataset. The notebook `algerian_dataset_cleaning.ipynb` performs data cleaning and preliminary exploratory data analysis (EDA). --- ## Table of Contents - Project Overview - Dataset - Notebook Workflow - Exploratory Data Analysis (EDA) - Insights & Findings - Technologies Used - Future Work - Author - References --- ## Project Overview Forest fires are complex events influenced by meteorological, climatic, and environmental factors. This project focuses on cleaning and understanding the dataset from **Bejaia** and **Sidi Bel-abbes** (Algeria), June–September 2012. The workflow aims to transform the raw data into a usable format, explore relationships, and generate insights that could support later prediction modeling. --- ## Dataset The dataset comes from the **Algerian Forest Fires Dataset** (UCI Machine Learning Repository). :contentReference[oaicite:0]{index=0} It contains meteorological data for two regions, collected daily from **June to September 2012**. :contentReference[oaicite:1]{index=1} **Key features include:** | Feature | Description | |-------------|-------------| | day, month, year | Date of observation | | Temperature | Average/noon temperature in °C | | RH | Relative Humidity (%) | | Ws | Wind Speed (km/h) | | Rain | Rainfall (mm) | | FFMC, DMC, DC, ISI, BUI, FWI | Fire Weather Index components | | Classes | Fire occurrence: `fire` or `not fire` | --- ## Notebook Workflow The main notebook is `algerian_dataset_cleaning.ipynb`. Here’s the high-level flow: 1. **Loading Data** - Read raw CSV(s) for Bejaia & Sidi Bel-abbes. 2. **Cleaning / Preprocessing** - Handle missing / malformed rows. - Rename columns for consistency. - Convert categorical classes if necessary. 3. **Exploratory Data Analysis (EDA)** - Statistical summaries. - Visualizations: distributions, correlations, etc. 4. **Insight Extraction** …