This dataset provides comprehensive information about transactions, with a particular focus on identifying fraudulent activities. With over 6 million entries, it offers a rich and diverse collection of transactional data for analysis and modeling.
This project focuses on developing a robust fraud detection system using machine learning techniques. The goal was to accurately identify fraudulent transactions within a financial dataset. https://colab.research.google.com/drive/1xdDxF2PkHSnRIu4vxQAAQ6M6I2vK-lCc?usp=sharing
Handling Missing Values: Implemented imputation techniques to address missing data. Data Splitting: Randomly split the dataset into training (80%) and testing (20%) sets to ensure proper evaluation.
Transformation and Categorization: Converted raw parameter values into numerical features to enhance model accuracy.
Model Selection: Trained multiple classifiers, including K-Nearest Neighbors, Logistic Regression, Decision Trees, and advanced ensemble methods. Imbalanced Data Handling: Addressed the significant imbalance between non-fraudulent and fraudulent cases using resampling and class weighting techniques.
Correlation Matrix: Analyzed relationships between features using a correlation matrix. Data Distribution: Visualized the distribution of transactions and fraud statuses.
High Accuracy: Achieved a remarkable accuracy of 99.8% in detecting fraudulent transactions, demonstrating the effectiveness of the implemented techniques.
This project highlights the importance of comprehensive data preprocessing, feature engineering, and the use of advanced machine learning models in achieving high accuracy in fraud detection tasks. The successful implementation of these techniques resulted in an exceptionally accurate fraud detection model.