├── Data └── default_of_credit_card_clients__courseware_version_1_21_19.xls ├── Lesson01 └── Exercise01.py ├── LICENSE └── README.md /Data/default_of_credit_card_clients__courseware_version_1_21_19.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrainingByPackt/Data-Science-Projects-with-Python/HEAD/Data/default_of_credit_card_clients__courseware_version_1_21_19.xls -------------------------------------------------------------------------------- /Lesson01/Exercise01.py: -------------------------------------------------------------------------------- 1 | for counter in range(5): 2 | ... print(counter) 3 | ... 4 | example_dict = {'apples':5, 'oranges':8, 'bananas':13} 5 | dict_to_list = list(example_dict) 6 | dict_to_list 7 | dict_to_list = dict_to_list + ['pears'] 8 | dict_to_list 9 | sorted(dict_to_list) 10 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Training By Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![GitHub issues](https://img.shields.io/github/issues/TrainingByPackt/Data-Science-Projects-with-Python.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python/issues) 2 | [![GitHub forks](https://img.shields.io/github/forks/TrainingByPackt/Data-Science-Projects-with-Python.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python/network) 3 | [![GitHub stars](https://img.shields.io/github/stars/TrainingByPackt/Data-Science-Projects-with-Python.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python/stargazers) 4 | [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python/pulls) 5 | 6 | # Data Science Projects with Python 7 | [Data Science Projects with Python](https://www.amazon.com/gp/product/1838551026) 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. 8 | 9 | [Data Science Projects with Python](https://www.amazon.com/gp/product/1838551026) by **Stephen Klosterman** 10 | 11 | ## What you will learn 12 | * Install the required packages to set up a data science coding environment 13 | * Load data into a Jupyter notebook running Python 14 | * Use Matplotlib to create data visualizations 15 | * Fit a model using scikit-learn 16 | * Use lasso and ridge regression to regularize the model 17 | * Fit and tune a random forest model and compare performance with logistic regression 18 | * Create visuals using the output of the Jupyter notebook 19 | * Use k-fold cross-validation to select the best combination of hyperparameters 20 | ### Hardware requirements 21 | For an optimal student experience, we recommend the following hardware configuration: 22 | * **Processor**: Intel Core i5 or equivalent 23 | * **Memory**: 4 GB RAM or higher 24 | * **Storage**: 35 Gb or higher 25 | 26 | ### Software requirements 27 | * 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 28 | * Browser: Google Chrome/Mozilla Firefox Latest Version 29 | * Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter notecourse on your browser) 30 | * Python 3.4+ (latest is recommended) installed (from https://python.org) 31 | * Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so on) 32 | 33 | --------------------------------------------------------------------------------