├── Data ├── Chapter_1_cleaned_data.csv └── default_of_credit_card_clients__courseware_version_1_21_19.xls ├── LICENSE ├── Lesson01 ├── Exercise01.py └── Lesson01.ipynb ├── Lesson02 └── Lesson02.ipynb ├── Lesson03 └── Lesson03.ipynb ├── Lesson04 └── Lesson04.ipynb ├── Lesson05 └── Lesson05.ipynb ├── Lesson06 └── Lesson06.ipynb └── README.md /Data/default_of_credit_card_clients__courseware_version_1_21_19.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning/30adf2fa8f28e9149fa2cbf8aba3924ecaedb2cb/Data/default_of_credit_card_clients__courseware_version_1_21_19.xls -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![GitHub issues](https://img.shields.io/github/issues/TrainingByPackt/Data-Science-Projects-with-Python-eLearning.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning/issues) 2 | [![GitHub forks](https://img.shields.io/github/forks/TrainingByPackt/Data-Science-Projects-with-Python-eLearning.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning/network) 3 | [![GitHub stars](https://img.shields.io/github/stars/TrainingByPackt/Data-Science-Projects-with-Python-eLearning.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning/stargazers) 4 | [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning/pulls) 5 | 6 | 7 | 8 | # Data Science Projects with Python 9 | Data Science Projects with Python will help you get comfortable with using the Python environment for data science. This course will start you on your journey to mastering topics within machine learning. These skills will help you deliver the kind of state-of-the-art predictive models that are being used to deliver value to businesses across industries. 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 reduce overfitting 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 | 20 | 21 | ### Hardware requirements 22 | For an optimal experience, we recommend the following hardware configuration: 23 | * **Processor**: Intel Core i5 or equivalent 24 | * **Memory**: 4 GB RAM 25 | * **Storage**: 35 GB available space 26 | 27 | 28 | 29 | ### Software requirements 30 | You'll also need the following software installed in advance: 31 | * OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu 32 | * Linux, or the latest version of OS X 33 | * Browser: Google Chrome/Mozilla Firefox Latest Version 34 | * Notepad++/Sublime Text as IDE (this is optional, as you can practice everything using the Jupyter Notebook on your browser) 35 | * Python 3.7+ (latest version of Python is recommended) installed (from https://python.org) 36 | * Python libraries as needed (Jupyter, NumPy, Pandas, Matplotlib, and so on) 37 | --------------------------------------------------------------------------------