🛡️ Credit Card Fraud Detection using K-Nearest Neighbors (KNN)

This project aims to detect fraudulent credit card transactions using the K-Nearest Neighbors (KNN) algorithm. The dataset used is highly imbalanced, with fraudulent transactions making up only a small fraction of the total. This project includes preprocessing, model training, evaluation, and optional deployment as a web app.

---

📊 Dataset

• Source: Kaggle - Credit Card Fraud Detection
• Note: The dataset contains 284,807 transactions with 31 features, including the target class.
• Disclaimer: Due to GitHub's 25MB file limit, the dataset is not uploaded directly here. Please download it manually from the link above.

---

📁 Project Structure

credit-card-fraud-detection-knn/ │ ├── data/ # 💾 Data folder (dataset to be downloaded manually from Kaggle) │ └── creditcard.csv # (not included in repo due to size limits) │ ├── notebooks/ # 📓 Jupyter Notebooks │ └── Credit Card Fraud Detection.ipynb # Main analysis and modeling notebook │ ├── models/ # 🤖 Trained and serialized ML models │ └── knn_model.pkl # Pickle file for the trained KNN model │ ├── app/ # 🌐 Web app files │ └── main.py # Streamlit app for real-time predictions │ ├── outputs/ # 📊 Outputs and plots │ ├── confusion_matrix.jpg # Evaluation visualizations │ └── classification_report # Detailed metrics │ ├── requirements.txt # 📦 Python dependencies ├── README.md # 📘 Project documentation ├── .gitignore # 🚫 Files to ignore in version control

🔍 Features & Techniques

• 📌 Data Preprocessing

  • Standardization of Amount and Time
  • Handling missing values and feature scaling

• 🧠 Model

  • K-Nearest Neighbors Classifier

• ⚖️ Imbalance Handling

  • Under-sampling of the majority class to improve fraud detection

• 📈 Evaluation

  • Confusion Matrix
  • Classification Report (Precision, Recall, F1-Score)
  • ROC-AUC Score

• 🖥️ Deployment

  • Streamlit app for real-time predictions

---

🚀 How to Run the Project

1. Clone the repository:

git clone https://https://github.com/Anarya22/Credit-Card-Fraud-Detection.git
cd Credit-Card-Fraud-Detection

2. Install dependencies(Optional as it is already in this repository): pip install -r requirements.txt
3. Download the dataset from Kaggle: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
4. Run the Jupyter Notebook: Credit Card Fraud Detection.ipynb
5. Run the Streamlit app: streamlit run main.py