Data Leakage Detection And Intelligent Data Preprocessing System

• Author(s):

  Pakirathan K | Murshith Ahamed Eithirish | Gokul | Gokul R

• Keywords:

  Data Leakage Detection, Intelligent Data Preprocessing, Machine Learning, Dataset Profiling, Target Leakage, Temporal Leakage, Data Visualization, Feature Engineering, Flask Web Application, Data Quality, Model Reliability, Automated Preprocessing.

• Abstract:

  Data Leakage Is One Of The Most Critical Challenges In Machine Learning Systems, Leading To Unrealistic Model Performance And Poor Generalization In Real-world Applications. Leakage Occurs When Information From Outside The Training Dataset Is Inadvertently Used During The Model Training Process, Causing Biased Predictions And Overly Optimistic Evaluation Metrics. Detecting Such Leakage Before Model Development Is Essential For Building Reliable And Robust Machine Learning Systems. This Paper Proposes A Data Leakage Detection And Intelligent Data Preprocessing System That Automatically Identifies Potential Leakage Sources In Datasets Prior To Model Training. The System Integrates Dataset Profiling, Leakage Detection, Preprocessing Techniques, And Visualization Tools Within A Flask-based Web Application. Users Can Upload Datasets, Analyze Data Quality, Detect Different Types Of Leakage Such As Target Leakage And Temporal Leakage, And Apply Safe Preprocessing Operations. The System Also Provides Interactive Data Visualizations And Exports A Cleaned Dataset Ready For Machine Learning Tasks. By Combining Leakage Detection With Automated Preprocessing, The Proposed Solution Improves Model Reliability, Reduces Human Error, And Enhances The Overall Machine Learning Workflow.

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Other Details

• Paper id:

  IJSARTV12I5105356

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-14

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