Handle Missing Data😵💫, Encoding & Feature Scaling in Python | ML Practical program
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In this video, we explore one of the most important topics in Machine Learning and Data Science: Data Preprocessing.
Before training any machine learning model, it is essential to clean and prepare the dataset properly. In this tutorial, you'll learn:
✅ Handling Missing Data using SimpleImputer
✅ Encoding Categorical Variables using OneHot Encoding
✅ Feature Scaling using StandardScaler
✅ Understanding the Output Step.
byStep
✅ Practical Program Explanation for Exams and Viva
This video is designed especially for beginners, students preparing for practical examinations, and anyone starting their Machine Learning journey.
By the end of this video, you'll understand not only how to write the code but also why each preprocessing step is necessary before building machine learning models.
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