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About Data cleaning with python for Data analytics and Modeling: Master the Art of Data Cleaning, Transformation, and Preparation for Robust Analytics and Machine Learning Models
In the world of data science, clean data is the foundation of accurate analysis and reliable machine learning models. and “Data Cleaning with Python and ” is your ultimate guide to mastering the essential techniques and tools for preparing data for analytics and modeling. Whether you’re a data analyst, data scientist, or machine learning engineer, this book equips you with the skills to handle messy, incomplete, and inconsistent datasets with confidence.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 Step-by-Step Learn how to clean and preprocess data using Python with hands-on examples.Real-World Case Apply your skills to real-world datasets, including the New York City Airbnb dataset and the Titanic dataset.Advanced Master advanced data cleaning techniques like text cleaning, date and time handling, and categorical data encoding.Automation and Best Discover how to automate data cleaning workflows and follow best practices for reproducible and scalable data preparation.Tools and 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, and “Data Cleaning with Python and ” is your go-to resource for transforming raw data into actionable insights.
Detailed Information
| Author: | Ahmed Khorshid |
|---|---|
| Publication Year: | 2024 |
| Language: | English |
| File Size: | 5.4 |
| Format: | |
| Price: | FREE |
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