Project 2 - Supply and Demand Gap Analysis

Supply and Demand Gap Analysis

Aim: Demand and supply gap analysis by using a uber data set.

Introduction: Uber is a platform where those who drive and deliver can connect with riders, eaters, and restaurants. In cities where Uber is available, you can use the Uber app to request a ride. When a nearby driver accepts your request, the app displays an estimated time of arrival for the driver heading to your pickup location.

The main objective of the project is to analyze the data on supply and demand gap analysis in the uber data set.

1. Count of status by Pickup Point and status
2. Count of Status by Day and Status
3. Count of Driver ID by Status
4. Count of Status by day and status by using a pie chart

Problem Statement

• We can find how many cars are available in City and Airport.
• We can find how many people completed the trip and canceled on different dates.
• We can also find how many cars are not available in different regions in a different time periods.

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

In this data interpretation step, I found out some important information about the data set.

• The data set was almost clear and completed.
• I replaced the null values with 0.
• There is a total of 6745 rows and 6 columns.

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 count some interesting points that are mentioned below:

Count of status by pickup point and status: In this clustered column chart we analyze the data that how many people were canceled in the city and airport, the number of cars that are not available in the city and airport, and the number of were completed the trip in city and airport. For example, In the city region, 1066 were canceled, and in the airport 198 were canceled.

Count of Driver ID by status: In this stacked column chart we analyze the data on which how many people completed the trip and how many people canceled the trip and also how many cars are not available. For example, 2831 people completed the trip, and 1264 people canceled the trip.

Count of status by day and status: In this line chart we analyze the data on which day the number of trips is completed and the number of cancelations is done also the number of cars not available on that particular day.

Count of status by day and status by using pie chart: In this pie chart we analyze the data on which day the percentage of trips is completed and the percentage of cancelations is done also the percentage of cars not available on that particular day.

Insights

• We analyze the data and identified that 2831 trips are completed in 5 working days and 1264 canceled the trip.
• We analyze the data and identified that 1504 completed the trip in the city and in the airport 1327 completed the trip.

Recommendations

• To avoid cancellation we have to reduce the cost of the traveling
• To increase the number of trips we have to increase the number of cars available.

Conclusion

• We conclude that almost 20% of people were canceled every day in both the city and the airport.
  • To avoid cancellation we have to reduce the cost of the traveling
    • We analyze the data and identified that 2831 trips are completed in 5 working days and 1264 canceled the trip.
    • We analyze the data and identified that 1504 completed the trip in the city and in the airport 1327 completed the trip.To increase the number of trips we have to increase the number of cars available.We conclude that almost 45% of people completed their trip in both the city and airport.