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
Stephen Klosterman is a machine learning data scientist at CVS Health. He enjoys helping to frame problems in a data science context and delivering machine learning solutions that business stakeholders understand and value. His education includes a Ph.D. in biology from Harvard University, where he was an assistant teacher of the data science course.
