Credit Card Fraud Detection using ML

The Project

This project is a classification model built using machine learning algorithms to detect fraudulent credit card transactions. It involves analyzing a dataset containing a large number of credit card transactions with a small percentage of fraudulent transactions. The main objective of the project is to develop a model that can accurately classify transactions as either fraudulent or legitimate. The project involves various steps including data preprocessing, exploratory data analysis, feature selection, model building, and evaluation. In the data preprocessing step, the dataset is cleaned, missing values are imputed, and outliers are removed. Exploratory data analysis involves visualizing the data to gain insights into the data distribution and relationships between the features. Feature selection involves identifying the most important features for the model. Model building involves training various machine learning algorithms on the dataset and selecting the best-performing model based on performance metrics such as accuracy, precision, recall, and F1-score. The final model is then evaluated on a test set to ensure that it generalizes well to unseen data. Once the model is finalized, it can be deployed to detect fraudulent transactions in real-time. This project was carried out using several steps. First, the dataset was obtained and preprocessed, which involved cleaning the data, handling missing values, and removing outliers. Next, the data was split into training and testing sets to build the machine learning models. Four different algorithms, namely, Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, were trained and evaluated using metrics such as accuracy, precision, recall, and F1 score. The models were fine-tuned by adjusting hyperparameters to improve their performance. The best-performing model was then selected based on its evaluation metrics, and its performance was further analyzed using the confusion matrix and Matthews Correlation Coefficient. Finally, the model was deployed using the Streamlit library as a web application, allowing users to interact with it and make predictions on new data. Overall, this project involved a comprehensive approach to building and evaluating a machine learning model for fraud detection, covering all essential steps from data preprocessing to model deployment.

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