Project 2 - Supply and Demand Gap Analysis
I Attached below the All Format Answer and Paste also in Text Box.
Then I will share my Project 2 Drive link, So kindly check my drive link
https://drive.google.com/drive/folders/117AWzilwXZQ_zggiknj_SSdygeXeVDmS?usp=sharing
Aim:
The primary aim of our project is to collect and analyze the data to
create visualizations which helps to understand the US Cab of Pink and
Yellow cab on 2016 -2018.
Introduction:
A cab, is a type of vehicle for hire with a driver, used by a
single passenger or small group of passengers, often for a non-shared
ride. A taxicab conveys passengers between locations of their choice.
This differs from public transport where the pick-up and drop-off
locations are decided by the service provider, not by the customers,
although demand responsive transport and share taxis provide a hybrid
bus/taxi mode.
Taxi service can be an important backup option for other alternative
forms of transport, such as allowing pedestrians to carry large loads
back from a store, providing an emergency ride home when a cyclist has
a medical or mechanical problem, or a Guaranteed Ride Home for a
rideshare or transit commuter. Informal taxi service often develops in
rural communities where certain motorists will drive their neighbor’s for
a fee. This data analyzes the difference between pink and yellow cab
company and give a brief understanding about the profit, users, traveled
distance and etc.
Problem Statement:
* Cab users in both yellow and pink in the year 2016 -2018.
* Percentage of distance covered by each company in the same city.
* Profit shared by the year 2016-2018.
* Distance traveled by each company in the year from 2016 to 2018.
* Price charged by both yellow and pink cab for each of the city and
the summary of maximum and minimum values.
Methodology:
* Data collection:
The data used in this analysis has been retrieved from kaggle
US Cab dataset 2016-2018.
* Data investigation:
In this step, organized and verified whether the data contains the
relevant fields that required to fulfill the problem statement, and
whether it satisfies the tools for the visualization.
* Data interpretation:
Data interpretation is the step in which to analyze and process the
data. Here, I made users trend, distance percentage, traveled
distance, profit, summary, price charged using the dataset.
* Visualization:
In this final step I’ve used 100% stacked column chart,
Line chart, area chart, donut chart, funnel to make the visualization
easier to read and understand.
Analysis:
In the given data set of US cab, we can simply understand that there
are two companies providing cab facility for some cities around US.
One is yellow cab and other is pink cab here I used the data from both of
these companies to analyze and to make visualization with the use of it.
The data contains the relevant fields about the charges made for a trip,
distance traveled and cost of trip with respect to the date. It makes easy
to understand and manipulate the data to create a better visualization.
And it is helpful to find the variations of profit and cost of trip and also
traveled distance for each of the city.
The data set used for the analysis.
Insights:
This line chart indicates a proper visualization about the amount of users
used the cab service in the interval of 2016-2018. By the use of this viz
it’s easy to know the exact values of users in the date.
This stacked column chart clearly shows the percentage of distance travel
by each company to the respected cities. We can see the difference of
percentage that each company shares in a city.
Here I used area chart to represent the distance travel by each company
by date and the distance mentioned in kilometer. Also we can find the
data for a specific date using slicer placed on the left side.
The donut chart indicated the value of profit earned by each country by
year and also we can differntiate the profit of each company with the
period of time as date or year as we prefered.
The relevant data about minimum and maximum of profit and cost of
trip showed here and also we can know the sum of users by date
by adjusting the slicer placed on the bottom center.
The price charged by each company’s data showed below here, I used
the funnel chart to easily understand the size variation between each of
the company.
Recommendations:
The analysis gave a better understating about the US Cab facility
data with the detailed visualization made up with the use of Power BI.
As you can see the above pictures shows that the most of the details of
dataset in a proper manner where we can get a clear understanding
about the data of Cab usage and related details in easy of approach.
And here we found the difference between each company from the
data that relates the users, travel distance, profit made by each company
and cost of trip also the price charged by each company.
With the representation we can know that there’s huge gap between
yellow and pink cab. The pink cab should provide more cab to cover more
users and also have to increase the charge of trip to get more or equal
profit compared with yellow cab.
Conclusion:
This is some of the detailed analysis and information of the US Cab
data of 2016-2018. From this I came to know the interesting facts about
the daily usage of cab services by each city and how yellow and pink cab
doing their major role in the cab service.
And also it helped to found the shortage of users in pink cab which
leads some lower while compared with yellow cab, and how it affects the
revenue and the market status of each company. And here I conclude that
I clear the problem statements with the help of power BI.