Problem Statement

The objective of this project is to develop a machine learning model capable of detecting fraudulent credit card transactions. Given the highly imbalanced nature of credit card transaction data (where fraudulent transactions are significantly fewer than legitimate ones), this project focuses on accurately identifying fraud while minimizing false positives.

Business Problem Overview

Credit card fraud poses a significant risk to financial institutions and consumers. Each year, billions of dollars are lost due to fraudulent activities. Detecting fraud promptly is crucial to mitigating financial losses and maintaining trust in financial systems. However, the detection process is challenging due to the large volume of transactions and the rarity of fraudulent activities. This project aims to address this challenge by leveraging machine learning techniques to build a robust fraud detection model that can differentiate between legitimate and fraudulent transactions.

Understanding and Defining Fraud

Fraudulent activities typically involve unauthorized transactions where the cardholder is unaware of the transaction until it appears on their statement. These activities could range from small, unnoticeable purchases to large, significant transactions. The primary goal of a fraud detection model is to flag transactions as fraudulent that deviate from a cardholder's usual behavior, ensuring minimal disruption to legitimate activities while identifying potential fraud effectively.

• Unauthorized Purchases: Transactions made without the cardholder’s consent.
  
• Card Not Present (CNP) Fraud: Fraudulent transactions conducted online without the physical card.
  
• Account Takeover: When a fraudster gains control over a cardholder’s account and makes unauthorized transactions.
  

Data Dictionary

The dataset link used for the analysis can be found on this link from Kaggle https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

The dataset used for this project typically includes the following features:

• Time: The number of seconds elapsed between this transaction and the first transaction in the dataset.
  
• V1, V2, …, V28: Principal Component Analysis (PCA) components obtained after dimensionality reduction of original transaction features for privacy reasons.
  
• Amount: The transaction amount.
  
• Class: The target variable, where 1 indicates a fraudulent transaction, and 0 indicates a legitimate transaction.
  

Project Pipeline

The project is structured into the following key stages:

1. Data Exploration and Preprocessing:

• Load and explore the dataset.
• Handle missing values and outliers.
• Normalize and scale features, particularly the Amount feature.

2. Handling Imbalanced Data:

• Explore class distribution.
• Apply techniques like SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset.

3. Model Selection and Training:

• Choose appropriate machine learning models such as Logistic Regression, Random Forest, or XGBoost.
• Train models on the balanced dataset.

4. Model Evaluation:

• Evaluate the models using metrics such as ROC-AUC, precision, recall, and F1-score.
• Use confusion matrices and ROC curves to analyze model performance.

5. Hyperparameter Tuning:

• Perform GridSearchCV or RandomizedSearchCV to optimize model parameters.

6. Final Model and Deployment:

• Select the best-performing model.
• Prepare the model for deployment, ensuring it can handle real-time transaction data.

7. How to Run

• Clone the repository.
• Install required libraries from requirements.txt.
• Run the Jupyter notebook or script for each stage of the project. Conclusion

7. Conclusion:

Summarize findings, model performance, and the potential impact on the business. The successful implementation of this project can significantly reduce financial losses due to credit card fraud by accurately detecting fraudulent transactions in real-time.