Aritro1204/ALGERIAN-FOREST-DATASET
Domain:
environment and energyRecord type:
dataset
Creator:
Ari
Host:
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.
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## 📌 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
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## 📂 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)
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## 🧹 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)
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## ⚖️ Feature Scaling
- Applied scaling to numerical features to normalize the range for model compatibility.
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📊 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 …