https://github.com/lmizner/codecademy_auto_evaluation
Summarized categorical variables in Python using numerical summary statistics.
https://github.com/lmizner/codecademy_auto_evaluation
categorical-data frequency jupyter-notebook median numpy pandas proportion python
Last synced: 4 months ago
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Summarized categorical variables in Python using numerical summary statistics.
- Host: GitHub
- URL: https://github.com/lmizner/codecademy_auto_evaluation
- Owner: lmizner
- Created: 2022-10-10T21:24:53.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-10T21:26:22.000Z (about 3 years ago)
- Last Synced: 2025-02-08T07:41:43.766Z (8 months ago)
- Topics: categorical-data, frequency, jupyter-notebook, median, numpy, pandas, proportion, python
- Language: Jupyter Notebook
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# codecademy_auto_evaluation
### Summarizing Automobile Evaluation Data
In the following project you’ll use what you’ve learned about summarizing categorical data to analyze a sample from a popular open source dataset. This dataset contains information on the cost and physical attributes of several thousand cars. Originally, this dataset was used for to train a classification model that assigned an acceptability score/category to cars based on these attributes.
The car evaluation dataset has been sourced from the UCI Machine Learning Repository and has been slightly modified for this project. Specifically, one additional field manufacturer_country has been simulated for illustrative purposes. You can read more about the details, features, and original uses of this dataset in research on the UCI data description page.