# Exploring for answers

This is a continuation of the previous lesson's [assignment](../14-Introduction/assignment.md), where we briefly took a look at the data set. Now we will be taking a deeper look at the data.

Again, the question the client wants to know: **Do yellow taxi passengers in New York City tip drivers more in the winter or summer?**

Your team is in the [Analyzing](README.md) stage of the Data Science Lifecycle, where you are responsible for doing exploratory data analysis on the dataset. You have been provided a notebook and dataset that contains 200 taxi transactions from January and July 2019.

## Instructions

In this directory is a [notebook](assignment.ipynb) and data from the [Taxi & Limousine Commission](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets). Refer to the [dataset's dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) and [user guide](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf) for more information about the data.


Use some the techniques in this lesson to do your own EDA in the notebook (add cells if you'd like) and answer the following questions:

- What other influences in the data could affect the tip amount?
- What columns will most likely not be needed to answer the client's questions?
- Based on what has been provided so far, does the data seem to provide any evidence of seasonal tipping behavior?


## Rubric

Exemplary | Adequate | Needs Improvement
--- | --- | -- |
