https://github.com/upul/chocolate-quality-analysis
This repository contains a Jupiter notebook which describes how to use basic machine learning tools such Scikit-Learning, Pandas, and Numpy for buiding models.
https://github.com/upul/chocolate-quality-analysis
machine-learning numpy pandas predictive-analytics scikit-learn
Last synced: 12 months ago
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This repository contains a Jupiter notebook which describes how to use basic machine learning tools such Scikit-Learning, Pandas, and Numpy for buiding models.
- Host: GitHub
- URL: https://github.com/upul/chocolate-quality-analysis
- Owner: upul
- Created: 2017-03-07T03:46:30.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-03-07T03:58:20.000Z (about 9 years ago)
- Last Synced: 2025-01-19T00:52:02.436Z (about 1 year ago)
- Topics: machine-learning, numpy, pandas, predictive-analytics, scikit-learn
- Language: Jupyter Notebook
- Size: 75.2 KB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Chocolate-Quality-Analysis
This repository contains a Jupiter notebook which describes how to use basic machine learning tools such Scikit-Learning, Pandas, and Numpy for buiding models.
The Jupiter notebooks describe how to do:
1. Loading and cleaning data
2. Build several models
3. Pick the best model based on K-Fold cross validation.
4. Run the model against the testing dataset.
5. Finally, conclusions and future directions.