{"repo":"AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised","clone":"git clone https://github.com/AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised.git","description":"Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.","language":"Python","stars":31,"topics":["data-analysis","data-science","financial-analytics","fraud-detection","logistic-regression","machine-learning","model-evaluation","portfolio-project","python","roc-curve"],"license":"MIT","category":"machine-learning","readme_excerpt":"Fraud Detection [SQL + Python (Supervised)] Predict fraudulent transactions using SQL (SQLite) for feature engineering and Python with Logistic Regression for supervised classification. --- Overview This project extends the unsupervised version by introducing labeled data and supervised learning . It demonstrates a complete fraud prediction pipeline, from SQL feature generation to model training, evaluation, and visualization. --- Workflow 1. Load labeled data into SQLite 2. Run SQL feature engineering - Compute per-user and daily transaction statistics 3. Train Logistic Regression model - Input: engineered SQL features - Output: fraud probability for each transaction 4. Evaluate model performance - AUC, Precision, Recall, F1-score 5. Visualize ROC curve --- Project Structure --- Dataset Schema Column Description --------- -------------- tx id Transaction ID user id Unique user identifier date Transaction date region User region merchant Merchant name amount Transaction amount label 1 = Fraudulent, 0 = Legitimate --- SQL Feature Engineering Feature generation reuses the same structure as the unsupervised project. --- Machine Learning Model: Logistic Regression - Trained on labeled transaction data - Balanced class weights for rare fraud cases - Evaluated using ROC AUC, precision, recall, and F1-score - Generates probability scores ( fraud proba ) for each transaction --- Visualization ROC Curve The ROC curve shows the trade-off between true positive rate (recall) and false po","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}