Aritro1204/ALGERIAN-FOREST-DATASET

The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria. # 🔥 Algerian Forest Fires Dataset – EDA, Cleaning & Preprocessing This project focuses on the **exploratory data analysis (EDA)**, **data cleaning**, **feature engineering**, and **preprocessing** of the Algerian Forest Fires dataset. The aim is to understand wildfire patterns and relationships between meteorological conditions and fire occurrences in two Algerian regions. --- ## 📌 Overview - **Dataset:** Algerian Forest Fires (2012) - **Instances:** 244 (122 from Bejaia, 122 from Sidi Bel-Abbes) - **Goal:** Analyze forest fire trends, clean the dataset, engineer features, scale data, and explore patterns for future ML modeling. - **Tools:** Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn --- ## 📂 Dataset Information | Feature | Description | |---------|-------------| | Date | From June to September 2012 | | Temperature | Max temp at noon (°C) | | RH | Relative Humidity (%) | | Ws | Wind speed (km/h) | | Rain | Daily rainfall (mm) | | FFMC, DMC, DC, ISI, BUI, FWI | Fire Weather Index components | | Classes | Fire / Not Fire (target variable) | - 🔢 11 input features + 1 output (`Classes`) - 🗺️ Two regions: Bejaia (0) and Sidi Bel-Abbes (1) --- ## 🧹 Data Cleaning & Preprocessing - Removed null values - Fixed column names and whitespace - Removed unneeded rows (like row 122) - Created a new column `Region` based on index - Converted string/object columns to numerical types - Encoded target classes (`Fire` → 1, `not fire` → 0) --- ## ⚖️ Feature Scaling - Applied scaling to numerical features to normalize the range for model compatibility. --- 📊 Exploratory Data Analysis (EDA) 🔍 Techniques Used: Histograms & Density Plots: Visualize feature distributions and detect skewness. Boxplots: Identify outliers in numerical features. Correlation Matrix & Heatmap: Explore relationships between variables. Pie Chart: Understand the distribution of target classes (Fire vs Not Fire). Monthly Fire Trend Plot: Analyze fire occurrence patterns over different m …