Uber Data Analysis Project

It's easy to give up on someone else's driving at times. Looking at the data, we can see that it is growing every day, with approximately 2.5 quintillion bytes of data being generated every day. There is less stress, more mental space, and more time to accomplish other things as a result. Now, from this data analysis, we can extract useful information that is most significant, and we can see that we are using Python to execute data analysis on Uber data. Yes, that is one of the concepts that expanded to become the basis for Uber and Lyft.

This is more of a data visualization project that will teach you how to use the ggplot2 library to better comprehend the data and create an intuition for the customers who book trips. So, before we get started, let's go over some basic data visualization concepts. You'll be able to solve any R programming task from the  data science course by the end of this blog..

Overview:

Uber is a multinational corporation with offices in 69 countries and over 900 cities worldwide. In the context of our Uber data analysis project, data storytelling is a key component of Machine Learning that allows businesses to comprehend the history of various operations. Lyft, on the other hand, is available in 644 cities across the United States and 12 locations in Canada. Companies can benefit from visualization by better comprehending complex data and gaining insights that will help them make better decisions. So, It is a great data science project idea for both beginners and experts.

However, it is the second-largest passenger airline in the United States, with a 31 per cent market share. You'll learn how to use ggplot2 on the Uber Pickups dataset and master the art of data visualization in R in the process.

Both services have comparable functions, from hiring a taxi to paying a bill. There is a lot of data in any firm. When the two passenger services reach the neck, however, there are some exceptions. By evaluating data, we can find key issues on which to work and prepare for the future, allowing us to make the best judgments possible. The same may be said regarding prices, particularly Uber's "surge" and Lyft's "Prime Time." Certain restrictions apply depending on how service providers are categorized.

The majority of organizations are moving online, and the amount of data generated is growing every day. Many publications focus on algorithm/model learning, data cleansing, and feature extraction without defining the model's objective. Data analysis is required to grow a firm in this competitive world. Understanding the business model can aid in the identification of problems that can be solved with the use of analytics and scientific data. Data analysis is sometimes required to help a company grow. The Uber Model, which provides a framework for end-to-end prediction analytics of Uber data prediction sources, is discussed in this article.

Importing the required libraries

We will import the necessary packages for this huge data analysis project in the first step of our R project. The following are some of the most significant R libraries that we will use:

●       gplot2: This is the project's backbone. ggplot2 is the most extensively used data visualisation package for creating visually appealing visualisation plots.

●       Ggthemes: This is a supplement to our core ggplot2 library. With this, we can use the mainstream ggplot2 tool to build more themes and scales.

●       lubridate: We will utilise the lubridate software to comprehend our data in different time groups. In the dataset, use time-frames.

●       dplyr: In R, this package is the de facto standard for data manipulation.

●       tidy: tidyr's core premise is to tidy the columns so that each variable has its own column, each observation has its own row, and each value has its own cell. Clean up the data.

●       DT: With the help of this package, we'll be able to interact with the Datatables JavaScript Library. In JS, you may create data tables.

●       scales: We can automatically map data to the relevant scales with well-placed axes and legends using graphical scales.

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Importing libraries and reading the data

 

import pandas as pd

import numpy as np

import datetime

import matplotlib

import matplotlib.pyplot as plt

import seaborn as sns

matplotlib.style.use('ggplot')

import calendar

 

Cleaning the data

 

data.tail()

 

Transforming the data

 

Getting an hour, day, days of the week, a month from the date of the trip.

 

data['START_DATE*'] = pd.to_datetime(data['START_DATE*'], format="%m/%d/%Y %H:%M")

data['END_DATE*'] = pd.to_datetime(data['END_DATE*'], format="%m/%d/%Y %H:%M")

 

Visualizing the data

 

Different categories of data. From the data, we can see most people use UBER for business purposes.

 

sns.countplot(x='CATEGORY*',data=data)

Final thoughts

We learned how to produce data visualizations at the end of the Uber data analysis R project. We used programmes like ggplot2, which allowed us to create a variety of visuals for various time periods throughout the year. We compare business vs. personal trips, the frequency for the purpose of the trip, the number of round trips, the frequency of the trip in each month, and so on, using the dataset. As a result, we were able to deduce how time affected customer travels. I hope you enjoyed the python Data Science Project described above. Continue to browse Learnbay: data science course in Bangalore, for additional projects involving cutting-edge technologies such as Big Data, R, and Data Science.