At a Glance

Name: Identify Data Leakage in Machine Learning Models
Price: Free CAD
Rating: 5.0 (1 reviews)
Author: joseph_santarcangelo, faranak_heidari, karan_goswami, victoria_nadar

Discover how to identify data leakage while implementing machine learning classifiers with real-world data. This project covers feature engineering and visualizing tree-based models to predict student dropout. Learn to build decision trees and random forests using Python, scikit-learn, and pandas, empowering you to make informed decisions. Designed for data science enthusiasts and professionals, this hands-on project sharpens your skills in handling classification challenges with real-world datasets. In just under 45 minutes, enhance your expertise and create impactful, data-driven outcome.

Discover how to identify and prevent data leakage while building machine learning models to predict student success. This project focuses on understanding and addressing data leakage, a critical challenge in machine learning that can compromise model validity. Additionally, the project emphasizes the importance of data preprocessing, feature engineering, and visualization to develop actionable models. Use Python, scikit-learn, and pandas to build decision trees and random forests that aid decision-making with data-driven insights.

Why this topic is important

Predicting whether students drop out or graduate has significant real-world implications, enabling early interventions and better educational outcomes. By completing this project, you’ll learn how to identify data leakage, a common but often overlooked issue in machine learning workflows, and explore how preprocessing and modelling can support decision-making. This project builds your technical skills while demonstrating the importance of data handling in solving real-world challenges.

A look at the project ahead

In this project, you’ll focus on detecting and addressing data leakage while using Python, scikit-learn, and pandas to analyze student data and predict outcomes. Learn practical techniques to build decision trees and random forests while ensuring your models remain realistic and reliable.

By the end of the project, you will:

Understand how to preprocess real-world datasets by identifying critical features prone to leakage and mapping data to ensure ethical and practical use.
Build and evaluate machine learning models like decision trees and random forests, with a focus on preventing data leakage and interpreting results using metrics such as accuracy, recall, and confusion matrices.
Recognize the signs of data leakage and implement techniques to mitigate its impact, ensuring model validity.

What you’ll need

To successfully complete this project, ensure you have:

A basic understanding of Python programming and familiarity with libraries such as Pandas and scikit-learn.
A web browser for accessing tools and running code.

Offered By: IBMSkillsNetwork

Identify Data Leakage in Machine Learning Models

At a Glance

Why this topic is important

A look at the project ahead

What you’ll need