Learn how to build a Credit Card Fraud Detection System using machine learning in Python and deploy it as an interactive web app with Streamlit! In this step-by-step tutorial, we’ll show you how to detect fraudulent transactions using powerful machine learning algorithms.
📌 What You’ll Learn in This Video:
✔ Data preprocessing and handling imbalanced datasets.
✔ Feature engineering techniques for fraud detection.
✔ Building and training machine learning models like Logistic Regression, Random Forest, and XGBoost.
✔ Evaluating model performance using precision, recall, and F1-score.
✔ Deploying the fraud detection system as a Streamlit web app.
📊 Topics Covered:
🔹 Exploratory Data Analysis (EDA) on financial transaction data.
🔹 Handling class imbalance using SMOTE or undersampling.
🔹 Model selection and hyperparameter tuning.
🔹 Developing an interactive UI with Streamlit for real-time fraud detection.
🚀 Tools and Libraries Used:
🟢 Python
🟢 Pandas, NumPy
🟢 Scikit-learn
🟢 LightGBM
🟢 Matplotlib, Seaborn
🟢 Streamlit
🟢 Geopy
🟢 SMOTE
💡 Who Is This Video For?
This tutorial is perfect for data science enthusiasts, and developers who want to learn practical fraud detection techniques and deploy machine learning models in real-world applications.
🔗 Links Mentioned in the Video:
🔹 Code Repository:https://github.com/TensorTitans01/Fraud_Detection_System
🔹 Dataset: https://drive.google.com/file/d/1118Jwzj51KpXd0T5jiebn9ykCygwbkhn/view?usp=sharing
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💬 Have Questions?
Drop a comment below, and we’ll be happy to help!
#CreditCardFraudDetection #MachineLearning #Python #FraudDetection #Streamlit #DataScience #TensorTitans
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