AmirFARES/Tunisia_Energy_Fraud_Detection_STEG

Detect and prevent electricity and gas fraud in Tunisia with data-driven insights. 🌐🔍 (zindi.africa) # Tunisia Energy Fraud Detection STEG ## Introduction 🌟 Combatting electricity and gas fraud in Tunisia 🇹🇳 for the Tunisian Company of Electricity and Gas (STEG). With losses reaching 200 million Tunisian Dinars, I achieved a top 25% position in the leaderboard using an XGBoost model with an AUC of 0.86. By analyzing client billing history, the solution aims to detect and curb fraudulent activities, safeguarding STEG's revenues and minimizing losses. ## Key Objectives 🎯 Detect and prevent fraudulent activities in electricity and gas consumption to enhance revenue and reduce losses. ## Data Sources 📊 All data is provided by the Tunisian Company of Electricity and Gas (STEG). You can access the data at **Zindi data section**. **File Descriptions:** - **train.csv** - Contains the target. This is the dataset used for model training. - **Fraud_Detection_Starter.ipynb** - This notebook helps you make your first submission for this challenge. - **Test.csv** - Resembles Train.csv but without the target-related columns. This is the dataset on which you will apply your model. - **SampleSubmission.csv** - Shows the submission format for this competition, with the ‘ID’ column mirroring that of Test.csv and the ‘target’ column containing your predictions. The order of the rows does not matter, but the names of the ID must be correct. - My Notebook **on kaggle** or **tunisia-energy-fraud-detection-steg.ipynb** ## Methodology 🚀 Approach: - Exploratory Data Analysis (EDA) on client and invoice data. - Correlation analysis, feature engineering, and aggregation to improve model performance. - Utilized an XGBoost classifier with tuning for optimal AUC. ## Data Preprocessing 🛠️ - Checked for NaN values. - Transformed data types. - And applied label and one-hot encoding to categorical columns. ## Model Architecture 🏗️ ```python model = XGBClassifier( n_estimators=4000, learning_rate=0.01, max_depth=3, objective='binary:logistic', random_state=42, scale_pos_weight=sum( …