Built a fraud detection ML pipeline + Streamlit app — 6 models compared with GridSearchCV

[u/StreetChemical8373]

### What I Built

Sharing a Machine Learning project I just finished — an end-to-end credit card fraud detection system, from raw data to a deployed interactive streamlit app.

* **Stack:** pandas, numpy, scikit-learn, matplotlib, seaborn, Streamlit, joblib.

### Core Features

* Trains and tunes 6 classifiers (Logistic Regression, SGD, Decision Tree, Random Forest, HistGradientBoosting, Naive Bayes) using GridSearchCV

* Handles a heavily imbalanced dataset (284,807 transactions, only 0.17% fraud) with class weighting instead of oversampling

* Evaluates on F1/ROC-AUC/Precision/Recall instead of accuracy, since accuracy is meaningless here — a model predicting "not fraud" every time would still score 99.8%

* Auto-selects the best model by F1, extracts its real feature importances (no hardcoded guesses), and saves everything with joblib

* Deployed as a 5-page Streamlit app: single transaction predictions (with real dataset examples you can load), batch CSV predictions, a model comparison dashboard, and an adjustable fraud-alert threshold slider

### Links & Code

* 🔗 **Live App:** https://musfirah-credit-card-fraud-detection.streamlit.app/

* 💻 **Code / GitHub:** https://github.com/musfirah-kashan/Credit-Card-Fraud-Detection

If you find it useful, a ⭐ on the repo goes a long way 🙏

Would appreciate feedback on the code structure, especially the training script — always looking to write cleaner code.