Project 2 - Supply and Demand Gap Analysis

Docs link: https://docs.google.com/document/d/1nCUNcQLU9bzb4e3mKTd2oIFzsCuosXy3zsUYImTlguo/edit?usp=sharing

Aim: To collect and analyze the data of Trip Pricing with Taxi Mobility Analytics site data and analyze it in detail by each Destination type, Gender, Type of Cab and Customer Rating, trip destination, etc. on that site.

Introduction: The main objective of this project is to analyze the data on, Trip pricing with taxi mobility. Through the Analysis of data, we can find out some important insights.

Problem Statement: We can find out how many males/females are on how many long destinations. Total pricing by gender and then by type of cab. In this analysis, we can see the number of cancellations in the last month, the Lifestyle index, etc.

Customers since month by destination type etc.

Methodology:

Step 1: Data cleaning

In data cleaning I cleaned the data with the transform data option present in Power BI because the dataset was not cleaned and included some missing values, so with the help of the duplicates function in Power BI, I cleaned the data and replace the null values with 0 and then started working on it.

Step 2: Data interpretation

I found some critical information about the data set in this data interpretation step.

• The data set was almost clear and completed.
• I replaced the null values with 0.
• There is a total of 131664 rows and 14 columns.
• Replaced one blank value with D.

Step 3: Visualization

In visualization, I took the help of Power BI Desktop software to make graphs and charts here some relevant graphs

and charts are attached.

Analysis:

In observation found some interesting points that are mentioned below.

• The Average customer rating is 3.03 for cab B and the Least rating is 2.72 for cab D.
• The highest Trip distance Covered is 1.29M by cab B.
• According to gender, the maximum is male.
• We can see the maximum value for surge price type 3 is 40803 for females.
• The surging price was higher for cab B and at Least for Cab E.
• The highest Cancellation was done for cab C and the least for cab E.
• Customers who are there for 10 months highly prefer cab B and least prefer cab E.

Insights:

• In conclusion, we can take a look at the final Dashboard for further analysis.
• We can see the values according to gender.
• The highest number of users for this app is Male.
• The preferred Cab is B and the least preferred Cab is E.
• The frequently canceled cab is C.

Recommendations:

• We can collect a large dataset for analysis.
• We can analyze the data of the trip destination.
• Like this dataset, we can perform operations with various types of cabs, gender, or destination type.
• We can work on large datasets and analyze them with the proper format of the chart.
• We can know more about the lifestyle index and confidence lifestyle index.

Conclusion:

In conclusion, we can take a look at the final Dashboard for further analysis.

As we can see in the Dashboard:

• The highest number of customers is Males.
• The most preferred cab is B.
• The most canceled cab is C.
• The least preferred cab is E.
• The surge price is higher for cab B.
• The surge price is minimum for Females.