> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/business-domains/examples.md).

# NYC TAXI

### [**NYC taxi pickup problem** ](http://www.vivekchoksi.com/papers/taxi_pickups.pdf)

[**The taxi problem**](http://www.vivekchoksi.com/papers/taxi_pickups.pdf) **is an intro to a well known machine learning problem, the paper will explain about feature engineering, analysis and using various regression algorithms for the purpose of solving the problem, you can use this as a base for many regression and classification problems.**<br>

**A** [**Second study**](http://blog.nycdatascience.com/student-works/predict-new-york-city-taxi-demand/) **(regression, random forest,** [**xgboost**](http://xgboost.readthedocs.io/en/latest/model.html) **(extreme gradient boosting tree)).**

[**Standard error estimate**](https://www.youtube.com/watch?v=r-txC-dpI-E\&index=4\&list=PLF596A4043DBEAE9C) **-- measures the distance from the estimated value to the real value**

**R^2 error estimate- measures the distance of the estimated to the mean against the real to the mean, 1 no error, 0 lots.**<br>

**\*\*\*\* with regression prediction it's best to create dummy variables (i.e., binary variables - exist or doesn't exist) from numeric variables, such as grid\_number to grid\_1, grid\_2 etc..**<br>


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