Data Cleaning & Analysis Project in Python | Pandas, Matplotlib, Seaborn Tutorial
In this tutorial, we walk you step by step through data cleaning and analysis of an e-commerce sales dataset using Python. Learn how to handle missing values, remove duplicates, fix incorrect data types, perform feature engineering, and visualize key insights with Seaborn and Matplotlib.
What you’ll learn in this video:
How to load and explore datasets with Pandas
Identify and handle missing or invalid data
Remove duplicates and fix data types
Perform feature engineering to create new insights
Visualize data: total sales by category, city, payment methods, and correlation heatmaps
Export clean datasets ready for analysis or reporting
Whether you’re a beginner in Python data analysis or looking to polish your data cleaning skills, this tutorial will guide you through a real-world e-commerce dataset step by step.
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