## 2.3 Exploratory data analysis

<a href="https://www.youtube.com/watch?v=k6k8sQ0GhPM&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR&index=14"><img src="images/thumbnail-2-03.jpg"></a>

[Slides](https://www.slideshare.net/AlexeyGrigorev/ml-zoomcamp-2-slides)


## Notes

**Pandas attributes and methods:** 

* `df[col].unique()` -> return a list of unique values in the series 
* `df[col].nunique()` -> return the number of unique values in the series 
* `df.isnull().sum()` -> return the number of null values in the dataframe 

**Matplotlib and seaborn methods:**

* `%matplotlib inline` -> assure that plots are displayed in jupyter notebook's cells
* `sns.histplot()` -> show the histogram of a series 
   
**Numpy methods:**
* `np.log1p()` -> apply log transformation to a variable, after adding one to each input value.

Long-tail distributions usually confuse the ML models, so the recommendation is to transform the target variable distribution to a normal one whenever possible. 

The entire code of this project is available in [this jupyter notebook](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/chapter-02-car-price/02-carprice.ipynb).  

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         The notes are written by the community. <br>
         If you see an error here, please create a PR with a fix.
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* [Notes from Peter Ernicke](https://knowmledge.com/2023/09/19/ml-zoomcamp-2023-machine-learning-for-regression-part-2/)

## Navigation

* [Machine Learning Zoomcamp course](../)
* [Session 2: Machine Learning for Regression](./)
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