Analysis of uber dataset to churn details
This Jupyter notebook contains a data analysis of the Uber drive 2016 dataset. The dataset contains information about over 50 million Uber rides in the United States. The notebook uses Pandas and Matplotlib to explore the data and answer some questions about Uber rides in 2016.
The following questions are answered in the notebook:
- What are the most popular times of day for Uber rides?
- What are the most popular days of the week for Uber rides?
- What are the most popular locations for Uber rides?
- What are the average fares for Uber rides?
The notebook also includes some visualizations of the data. These visualizations can be used to answer the questions above and to gain a better understanding of Uber rides in 2016.
To run the code in this notebook, you will need to install Python and the following libraries:
- Pandas
- Matplotlib
- Seaborn
- Numpy
- datetime
Once you have installed the libraries, you can open the Jupyter notebook viewer and navigate to the directory containing the notebook. You can then open the notebook and run the code.
The following is a list of the attributes and information about the data:
- Date
- Time
- Pickup location
- Dropoff location
- Fare
- Distance
- Product type
- Payment type
- Driver ID
This dataset was collected by Uber and made available to the public. The dataset can be found on the Uber website.