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Gain hands-on experience with industry-standard data analysis and machine learning tools in PythonKey FeaturesLearn techniques to use data to identify the exact problem to be solvedVisualize data using different graphsIdentify how to select an appropriate algorithm for data extractionBook DescriptionData Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these 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 unstructured 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 forIf 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
eBook details
- Title: Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn
- Author : Stephen Klosterman
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- Genre: Kindle Store,Kindle eBooks,Computers & Technology
- Pages : * pages
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