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

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python

Key Features

Learn techniques to use data to identify the exact problem to be solved

Visualize data using different graphs

Identify how to select an appropriate algorithm for data extraction

Book 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 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 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

Who 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 Contents

Data Exploration and Cleaning

Introduction to Scikit-Learn and Model Evaluation

Details of Logistic Regression and Feature Exploration

The Bias-Variance Trade-off

Decision Trees and Random Forests

Imputation of Missing Data, Financial Analysis, and Delivery to Client

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