In this video, we cover Data Preprocessing in Machine Learning using Python (Google Colab) step by step. Data preprocessing is the most important step in any ML project, and here I’ll show you how to clean, transform, and prepare raw data before training models.
📌 What you’ll learn in this video:
Data Cleaning (handling missing values, duplicates, inconsistent data)
Encoding categorical features
Feature Scaling (Normalization & Standardization)
Feature Selection & Dimensionality Reduction basics
Splitting data into Training & Test sets
By the end of this tutorial, you’ll understand how to preprocess datasets for Machine Learning models efficiently.
👉 Tools used: Python, Pandas, NumPy, Scikit-learn, Google Colab
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