Data cleaning with python for Data analytics and Modeling

This comprehensive guide covers everything from the basics of data cleaning to advanced techniques, including handling missing data, removing duplicates, detecting outliers, and transforming data for machine learning. With practical examples, step-by-step tutorials, and real-world case studies, you'll learn how to use Python's powerful libraries like Pandas, NumPy, Scikit-learn, and OpenRefine to clean and preprocess data efficiently.
Key Features:Step-by-Step Tutorials: Learn how to clean and preprocess data using Python with hands-on examples.Real-World Case Studies: Apply your skills to real-world datasets, including the New York City Airbnb dataset and the Titanic dataset.Advanced Techniques: Master advanced data cleaning techniques like text cleaning, date and time handling, and categorical data encoding.Automation and Best Practices: Discover how to automate data cleaning workflows and follow best practices for reproducible and scalable data preparation.Tools and Libraries: Explore Python's ecosystem of data cleaning tools, including Pandas, NumPy, Scikit-learn, and OpenRefine.Whether you're preparing data for analytics, building machine learning models, or ensuring data quality for business insights, "Data Cleaning with Python" is your go-to resource for transforming raw data into actionable insights.