Handling Imbalanced Dataset in Machine Learning: Easy Explanation for Data Science Interviews

Imbalanced Data is one of the most common machine learning problems you’ll come across in data science interviews. In this video, I cover what an imbalanced dataset is, what disadvantages it presents, and how to deal with imbalanced data when data contains only 1% of the minority class. 🟢Get all my free data science interview resources www.emmading.com/resources 🟡 Product Case Interview Cheatsheet www.emmading.com/product-case-cheat-sheet 🟠 Statistics Interview Cheatsheet www.emmading.com/statistics-interview-cheat-sheet 🟣 Behavioral Interview Cheatsheet www.emmading.com/behavioral-interview-cheat-sheet 🔵 Data Science Resume Checklist www.emmading.com/data-science-resume-checklist ✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: www.emmading.com/coaching // Comment Got any questions? Something to add? Write a comment below to chat. // Let's connect on LinkedIn: www.linkedin.com/in/emmading001/ ==================== Contents of this video: ==================== 00:00 Introduction 01:20 Interview Questions 01:38 Imbalanced Data 03:15 Why it causes problems? 04:27 How to deal with imbalanced data? 08:13 Model-level methods 11:33 Evaluation Metrics 13:25

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