Learn how to build a Credit Card Fraud Detection System using Machine Learning and Data Science in Python! 🚀
In this step-by-step project, we’ll use real-world credit card transaction data to detect fraudulent activities using various ML algorithms like Logistic Regression, Random Forest, and XGBoost.
GitHub Link for this repository: https://github.com/nightfury217836/Credit-Card-Fraud-Detection.git
This project is perfect for data science and machine learning beginners looking to build a real-world, resume-worthy project that demonstrates practical skills in data preprocessing, feature engineering, model training, and evaluation.
🧠 What You’ll Learn:
Data cleaning and preprocessing techniques
Handling imbalanced datasets (SMOTE, undersampling, oversampling)
Exploratory data analysis (EDA) and visualization
Model building and performance evaluation (Accuracy, Precision, Recall, F1-Score, ROC Curve)
Deploying a fraud detection model
🧩 Tools & Libraries Used:
Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, XGBoost
💼 Project Type:
Machine Learning | Data Science | Python | Credit Card Fraud Detection
🔔 Don’t Forget To:
👍 Like | 💬 Comment | 🔔 Subscribe for more AI, ML, and Python projects: @SouvikChai
📢 Share this project with your friends who are into Data Science & Healthcare AI!
