Uber Data Analysis

Published on 16 Apr 2022. Written by Amitesh Kumar

This analytics project is very component to understand the use of data analytics. Through projects like this, many companies can understand various complex operations. Uber Data Analysis project enables us to understand the complex data visualization of this huge organization. It is developed with the help of ‘R’ programming language. Let’s get started with the project.

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SLNOTE


Project implementation

The first step as always lies with importing the big data sets from the internet to our programming language platforms, such as ggplot2, ggthemes, lubridate, dplyr, tidlyr, DT, and scales. Let’s get a look over these libraries and how they are implemented in the project.

• Ggplot2 - it is the main part of the project and it is used widely to create aesthetic visualization plots.
• Ggthemes – it is a library for many themes from which the user can get the desired scale for their database.
• Lubridate – it consists of time frames and it should be in separate time categories.
• Tidyr – This function will classify the huge data into many columns and rows which will make it easier to manipulate it.
• DT – This will help in creating an interface between the program and javascript.

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Then the data is fed to the system, we can also choose any color from the wide range of colors. The CSV files are read from around 6 months of range. The data is for the number of passengers during a particular hour of the day so that the company can easily track its traffic. The greater the number of passengers, the greater is the demand for the number of cars.

Through various studies, it has been found the maximum number of passengers is from 5:00 Pm to 6:00 Pm. The graph shows a good knowledge of the ups and downs in the booking of the Uber. The R language will facilitate us to create a graph with different color ranges to show differences among the passengers. In this way, we can track the number of passengers in a month or year. The visual reports will be more attractive and explainable.

Creating a heatmap visual for day, month and hour will be the real data representation. The Ggplot () function will help creating maps for hour, month, and daily basis. For example, we can create the project for New York City that how many times Uber is booked for a particular day or month. This project will help in understanding the concept of data manipulation and extracting information from huge databases.

Conclusion

The project of Uber data analysis is finally completed and for this, the developer should know about the basics of R language. Data visualization makes it easier to understand the core values of the databases. Data science is very interesting and this is one of the projects which prove it. This project is easily implemented and very useful for a number of apps. Not only Uber but there is a lot more application which will need to extract information from their huge databases. This project can help in that situation.

SLDYK
Kit required to develop Uber Data Analysis:

• No kit

Technologies you will learn by working on Uber Data Analysis:

• Data Mining
• R programming

Any Questions?