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Data Science Projects with Python
Data Science Projects with Python

Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

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Profile Icon Barbora stetinova
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Course Jun 2019 367 pages 1st Edition
Arrow left icon
Profile Icon Barbora stetinova
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Course Jun 2019 367 pages 1st Edition

Key benefits

  • Practical guidance on industry-standard data analysis and machine learning tools
  • Tune algorithms to provide the best predictions on new unseen data
  • Gain knowledge on how to prepare data and feed it to machine learning algorithms
  • Gain insight into the predictive capabilities of the models and the way they make predictions

Description

Data 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 course 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. The codes for this course can be downloaded from https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning.

Who is this book 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 Python and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.

What you will learn

  • Install the required packages to set up a data science coding environment
  • Load data into a Jupyter Notebook running Python
  • Use Matplotlib to create data visualizations
  • Fit a model using scikit-learn
  • Use lasso and ridge regression to reduce overfitting
  • Fit and tune a random forest model and compare performance with logistic regression
  • Create visuals using the output of the Jupyter Notebook

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 28, 2019
Length: 367 pages
Edition : 1st
Language : English
ISBN-13 : 9781838986063
Category :
Languages :
Concepts :

Product Details

Publication date : Jun 28, 2019
Length: 367 pages
Edition : 1st
Language : English
ISBN-13 : 9781838986063
Category :
Languages :
Concepts :

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Table of Contents

6 Chapters
Data Exploration and Cleaning Chevron down icon Chevron up icon
Introduction to Scikit-Learn and Model Evaluation Chevron down icon Chevron up icon
Details of Logistic Regression and Feature Exploration Chevron down icon Chevron up icon
The Bias-Variance Trade-off Chevron down icon Chevron up icon
Decision Trees and Random Forests Chevron down icon Chevron up icon
Imputation of Missing Data, Financial Analysis, and Delivery to Client Chevron down icon Chevron up icon
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