Contents
Lesson 1: Data Cleaning
Data cleaning is the process of preparing raw data so it can be used reliably for analysis. Real-world student datasets may contain missing values, inconsistent entries, or incorrect formats that can lead to misleading results if not handled properly.
In this project, data cleaning is important because student-related information such as attendance, sleep hours, or tutoring sessions may contain missing or irregular values. Cleaning ensures that the dataset remains consistent and suitable for analysis, visualization, and modeling.
Improving Data Quality: Removes inconsistencies that could distort performance insights.
Preventing Analysis Errors: Ensures calculations such as averages and correlations work correctly.
Preparing Data for Analysis: Makes the dataset structured and ready for EDA and machine learning.
Data cleaning is always the first step before performing meaningful analysis.










