SKU/Artículo: AMZ-1838551026
Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
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Paperback
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En stock
En stock
Peso con empaque:
0.68 kg
0.68 kg
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Sí
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Nuevo
Nuevo
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Amazon
Amazon
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USA
USA
Sobre este producto
- Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key FeaturesLearn methods to identify potential data issues and solve themCreate effective visualizations using histograms, scatter and line plots, and other graphsIdentify the appropriate mathematical model for a given problem, train, and test itDevelop the communication skills needed to execute successful projects that create valueBook Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, while using realistic data. This book takes a case study approach to illustrate the end-to-end data science project pipeline, from obtaining data and communicating with business partners, through data exploration and model development, to characterizing the financial value that a model can create. Along the way, you will be guided through how to use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, in order to identify and correct potential data issues. You will then learn how to prepare data and feed them to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune these algorithms to provide the best predictions on new and unseen data. As you delve into later chapters, you'll be able to understand the workings and output of these algorithms and gain insight into not only the predictive capabilities of the models but also the math behind the predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from real-world data. What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter Notebook running PythonUse Matplotlib to create data visualizationsFit a model using scikit-learnUse lasso and ridge regression to reduce overfittingFit and tune a random forest model and compare performance with logistic regressionCreate visuals using the output of the Jupyter NotebookWho this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Table of ContentsData Exploration and CleaningIntroduction to Scikit-Learn and Model EvaluationDetails of Logistic Regression and Feature ExplorationThe Bias-Variance Trade-offDecision Trees and Random ForestsImputation of Missing Data, Financial Analysis, and Delivery to Client
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AR$270.490
60% OFF
AR$108.196
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