1 option
SN Video coding and web development. Machine learning with regression in Python / Springer.
- Format:
- Video
- Series:
- Academic Video Online
- Language:
- English
- Subjects (All):
- Python (Computer program language).
- Machine learning.
- Artificial intelligence.
- Regression analysis.
- Genre:
- Instructional films.
- Physical Description:
- 1 online resource (45 minutes)
- Other Title:
- Machine learning with regression in Python
- Springer Nature video coding and web development
- Place of Publication:
- London, England : Springer Nature, 2020.
- Language Note:
- In English.
- System Details:
- video file
- Summary:
- In this video, you will learn regression techniques in Python using ordinary least squares, ridge, lasso, decision trees, and neural networks. We start by exploring a census dataset that captures sales from a business in various counties across the United States. We briefly explore the dataset before moving onto model assumptions and feature engineering. We then implement a linear regression, which is a simple model that is easy to interpret, then move through more complex models to see what best makes predictions on our dataset. To avoid overfitting, we split our dataset and to optimize predictions, we tune hyperparameters with k-folds cross validation. We move through models that are more complex until we arrive at a neural network model. We then use the model with the lowest error metrics on the test dataset and make predictions on a new dataset. Using these predictions, we make a recommendation to the company's shareholders who want to expand the business about which counties to expand to next. This modeling process will be done in Python 3 on a Jupyter notebook, so it's a good idea to have Anaconda installed on your computer so you can follow along. We will structure our notebook to be easy-to-read by others on our team who may want to expand on our analysis.
- Notes:
- Title from resource description page (viewed March 9, 2021).
- OCLC:
- 1245589573
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.