Data Cleaning(Beginner - Advanced): Practical Projects | Python Pandas Tutorial | Real World Dataset

NOTE: Find Datasets below

Welcome to the Data Cleaning Marathon! In this Python tutorial, I take you through the real-world process of cleaning 4 different messy datasets using powerful data wrangling techniques in Python and Pandas.

If you've ever struggled with:

✅ Missing values

✅ Duplicates and inconsistent formats
✅ Dirty columns and mixed data types
✅ Urls, and HTML tags in rows

…this video is for YOU!

Recommended Videos:

1. PowerBI Project Tutorial: https://www.youtube.com/watch?v=aT0t0KQdP5gu0026t=5620s
2. Data Modelling for Beginners: https://youtu.be/PXEy9V6zNko
3. Interview questions for Data Professionals: https://youtu.be/mk-0sbPsI2c

Data cleaning is a crucial step in data analysis, ensuring accurate, high-quality data for decision-making. This tutorial covers everything from data cleaning to a hands-on project where we clean messy datasets using Python!

You'll learn step-by-step how to:

✅ Clean dirty CSV and Excel files

✅ Handle missing values, outliers u0026 nulls

✅ Format dates, strings, and numbers

✅ Handling URL and HTML tags in rows using requests and BeautifulSoup in Python

✅ Use Pandas, NumPy, Missingno, and Python best practices

✅ Prepare data for analysis or machine learning

🔽 Timestamp:
00:00 - Introduction
01:02 - Overview of the 4 Messy Datasets
02:45 - Dataset 1: Cleaning Sales Data
08:30 - Dataset 2: Fixing Customer Info Table
14:10 - Dataset 3: Cleaning Financial Transactions
20:50 - Dataset 4: Repairing Product Inventory Sheet
26:45 - Common Mistakes to Avoid in Data Cleaning
28:30 - Summary: Best Practices u0026 Takeaways
30:00 - Final Thoughts + What’s Next


💡 Whether you're a beginner data analyst, data scientist, or Python enthusiast, this tutorial will help you master data cleaning for better analysis and machine learning models.

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👉 Datasets: https://drive.google.com/drive/folders/10LpZHUSCYD-xidoenqCe2NowHyqzMxca?usp=sharing

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