https://github.com/tansudasli/analytics-sandbox

from Statistical approach to Machine learning
https://github.com/tansudasli/analytics-sandbox

feature-engineering machine-learning matplotlib numpy opencv pandas probability regex scikit-learn seaborn statistics

Last synced: 2 months ago
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from Statistical approach to Machine learning

• Host: GitHub
• URL: https://github.com/tansudasli/analytics-sandbox
• Owner: tansudasli
• Created: 2019-11-29T08:21:44.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-02-05T14:12:10.000Z (over 6 years ago)
• Last Synced: 2025-03-01T04:27:08.713Z (over 1 year ago)
• Topics: feature-engineering, machine-learning, matplotlib, numpy, opencv, pandas, probability, regex, scikit-learn, seaborn, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 68.9 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# analytics-sandbox

Machine learning models w/ scikit-learn

## structure

- / (root): **ML** on real scenarios

- /dataset: .csv files

- /probability: core probability exercises

- /statistics: core statistic exercises

- /pandas: core pandas concepts, intersection b/w pandas and numpy

- /opencv: core image and video concepts w/ opencv

- /numpy: core _image concepts_ in matplotlib and numpy

- /scikit-learn: core _ML_ concepts w/ scikit-learn

## models at / (root)

- `car_sales_lineer_regression` covers all ML steps

- `advertising_EDA` uses precleaned **click dataset**.

- real estate in NYC is **very massy**! dataset. 

   - `real_estate_of_nyc_EDA`, covers explatory data analysis

   - `real_estate_of_nyc_lineer_regression`, sale_price _prediction_

   - `real_estate_of_nyc_knn`, to _set empty_ neighborhoods