Sales Forecasting: Fighting Data Leakage

Overview

This project demonstrates the challenges of data leakage in machine learning, using a sales forecasting dataset. Data leakage can lead to overly optimistic model performance, making it appear as though the model predicts with near-perfect accuracy, while in reality, it utilizes information unavailable during actual predictions.

Through this project, I highlight:

• How data leakage can significantly skew model results.
• Methods to detect and mitigate leakage.
• The importance of critical thinking when interpreting model performance.

Key Takeaways

1. Data Leakage Identified:
   During the development process, I raised concerns about potential data leakage in the dataset. Upon investigation, it became evident that the dataset contained information unavailable at prediction time, leading to artificially inflated performance metrics.

2. Dataset Revision:
   After reporting this issue, the dataset was revised to remove the leakage. As a result:

   • The previous results achieved with this notebook cannot be replicated with the updated dataset.
   • This notebook serves as an educational example of how leakage can occur and why addressing it is critical.

3. Awareness is Key:
   This project underscores the necessity of carefully analyzing datasets to avoid falling into the trap of believing predictions with unrealistic accuracy (e.g., ~100%). Awareness of leakage ensures more robust and generalizable models.

Project Features

• Exploratory Data Analysis (EDA):

  • Insights into sales trends and patterns.
  • Initial preprocessing steps to clean and prepare the data.

• Machine Learning Models:

  • Experimented with 10 different algorithms, including regression and ensemble techniques.
  • Compared model performance metrics before and after addressing leakage.

• Anti-Leakage Practices:

  • Demonstrates the use of strict validation strategies.
  • Highlights the pitfalls of including future information in training datasets.

Usage

This notebook is shared to:

1. Educate practitioners about the dangers of data leakage.
2. Showcase techniques for identifying and addressing leakage.
3. Provide a framework for building reliable and realistic forecasting models.

Note: The results from this notebook cannot be replicated with the updated dataset due to the removal of leaked information.

Acknowledgments

I would like to thank the dataset providers for promptly addressing the leakage issue after it was reported. This proactive step ensures more reliable research and analysis for the community.

Lessons Learned

• Always critically evaluate datasets for potential issues.
• Validate models rigorously using proper cross-validation techniques.
• Avoid relying solely on high performance metrics; question their validity and context.