Saved searches

Use saved searches to filter your results more quickly

Cancel Create saved search Sign up Reseting focus

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

License

Notifications You must be signed in to change notification settings

TrainingByPackt/Data-Science-Projects-with-Python

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Go to file

Folders and files

Last commit message Last commit date

Latest commit

History

View all files

Repository files navigation

Data Science Projects with Python

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. Data Science Projects with Python by Stephen Klosterman

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 regularize the model
Fit and tune a random forest model and compare performance with logistic regression
Create visuals using the output of the Jupyter notebook
Use k-fold cross-validation to select the best combination of hyperparameters

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

Processor: Intel Core i5 or equivalent
Memory: 4 GB RAM or higher
Storage: 35 Gb or higher

Software requirements

OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
Browser: Google Chrome/Mozilla Firefox Latest Version
Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter notecourse on your browser)
Python 3.4+ (latest is recommended) installed (from https://python.org)
Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so on)

About

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn