Join my Academy, learn Data & AI skills and land a job👇 Data cleaning with Python seems straightforward, but ironically, real-world errors and debugging make it anything but simple. In this video, I walk you through an end-to-end data cleaning process in Python using Google Colab, tackling messy data issues like inconsistent formats, incorrect data types, duplicates, and null values - the real challenges data pros face daily. The dataset that I used and whole code is available here 📸 SOCIAL Instagram and TikTok - @Loresowhat @loresowhat 📩 Get in touch: loresowhat@ 🔎 CONTENT 0:00 Introduction to Data Cleaning with Python 2:33 Project Setup and Dataset Overview 5:42 Cleaning Headers and Column Names 8:28 Cleaning and Formatting 'Spend' Column 11:42 Correcting Categorical Typos in 'Channel' Column 14:16 Handling Mixed Boolean Values in 'Active' Column 15:54 Converting Dates to Proper Format 17:19 Data Integrity Check: Clicks vs Impressions 19:17 Logical Integrity: Start Date vs End Date 21:34 Handling Outliers in 'Spend' Column 24:40 Feature Extraction from 'Campaign Name' 26:10 Conclusion and Summary of Data Cleaning Steps #DataCleaning #PythonTutorial #googlecolab