Credit Card Fraud Detection

🚀 End-to-End Machine Learning Project using Logistic Regression, Random Forest, and advanced data preprocessing to detect fraudulent credit card transactions.

---

📌 Project Overview

Credit card fraud is a significant problem for financial institutions and customers.
This project builds a machine learning model to classify transactions as fraudulent or legitimate using anonymized transaction data.

---

📂 Dataset

The full dataset is too large to upload here.
You can download it from Kaggle:
🔗 Credit Card Fraud Detection Dataset

After downloading, place the creditcard.csv file in the project folder before running the notebook.

---

🛠️ Tech Stack

• Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Joblib
• Tools: Jupyter Notebook, GitHub

---

📊 Workflow

1. Data Preprocessing
   • Handling imbalanced dataset
   • Feature scaling
2. Model Building
   • Logistic Regression
   • Random Forest Classifier
3. Model Evaluation
   • Accuracy
   • Precision, Recall, F1-Score
   • Confusion Matrix
4. Model Saving
   • Serialized with joblib for deployment

---

📈 Results

• Best Model: Random Forest Classifier
• Precision: 0.98
• Recall: 0.96
• F1 Score: 0.97

---

git clone https://github.com/HemanthLove/Credit-Card-Fraud-Detection.git cd Credit-Card-Fraud-Detection