This project focuses on detecting fraudulent transactions in credit card data using various machine learning techniques. The goal is to identify potential fraudulent activities and minimize financial losses by accurately predicting fraud cases.
Credit card fraud poses a significant challenge to financial institutions and consumers. The goal of this project is to build a predictive model that can accurately classify transactions as either legitimate or fraudulent. The model is trained on a dataset of historical transactions and is designed to handle the highly imbalanced nature of fraud detection.
- Data Preprocessing: Cleaning and preparing the data, including handling missing values, normalizing features, and encoding categorical data.
- Exploratory Data Analysis (EDA): Detailed analysis to understand the distribution of transactions, detect outliers, and identify key features.
- Modeling: Implementation of several machine learning algorithms such as Isolation Forest Algorithm , Random Forest, SVM , and Local Outlier Factor (LOF) Algorithm.
- Model Evaluation: Evaluation of models using accuracy, precision etc to identify the best-performing model.