Data Cleaning | Missing Values, Noise & Outliers | Data Preprocessing | Lec. 04

In this lecture, we study Data Cleaning, a crucial step in Data Preprocessing, focusing on handling missing values, noise, and outliers. These concepts are essential for improving data quality before analysis and are important for GATE DA. Data Cleaning 📌 Topics Covered 🔹 Missing Values • Types of missing data • Handling techniques (ignore, imputation, mean/median, etc.) 🔹 Noise in Data • What is noise? • Noise handling techniques – Binning – Smoothing 🔹 Outliers • What are outliers? • Detection methods • Practical examples and intuition 🎯 Why this topic is important? Clean data leads to better analysis, accurate models, and reliable insights. 🎯 Important for: GATE DA Data Preprocessing Data Analysis 📌 Data cleaning is a fundamental step before transformation and modeling.