Project 2 - Supply and Demand Gap Analysis

Project 2 - Supply and Demand Gap Analysis

Aim: The main aim of project is to collect and analyze the data

Introduction: Supply and demand gap analysis is a comprehensive assessment of the disparities between the availability of rideshare vehicles (supply) and the number of ride requests (demand) in a given market or geographic area. By examining this gap, Uber can identify areas of opportunity for expansion, improve operational efficiency, enhance customer satisfaction, and increase driver earnings.

The supply and demand gap analysis involves collecting and analyzing a vast array of data, including historical ride data, driver availability, customer demand trends, geographical information, and various external factors influencing transportation patterns (e.g., weather, events, holidays). This analysis allows Uber to identify areas where demand exceeds supply, resulting in longer wait times and unmet customer needs, or areas where supply exceeds demand, leading to reduced driver earnings and inefficient resource allocation.

To bridge the supply and demand gap, Uber can leverage the insights gained from the analysis to implement targeted strategies. This may involve adjusting driver incentives, increasing or reducing the number of available drivers in specific areas or time periods, introducing surge pricing during peak demand, or offering promotions to encourage user engagement. Such measures aim to align supply and demand more effectively, ensuring a seamless experience for both drivers and passenger.

Problem Statement : In this project, we find out how many request id is placed from the pickup point of the Airport and city, How many Drivers are present, How many trips are completed, and how many trips are cancelled also find out how many trips the car is not available for the trips.

Methodology :

• The first column is request id it is an individual id placed by the user.
• Pickup point : there are two pickup point those are city and airport
• Driver id : each driver have his/her own driver id.
• Status : it represent completed , no cars available, cancelled
• Request timestamp: it represent when the customer requested uber
• Drop timestamp: when the customer was dropped.

Dataset does not contain much null values it has null at where driver was not available.

As the same way if the driver not available means there is no drop timestamp.

Wherever null values present we inserted 0

Visualization:

• First sheet represent how many drivers are present by using distinct count value on driver id.
• There are 300 unique drivers are present, and 1 in not available inserted as 0.
• Below picture represent how many orders are placed from airport or city in that how many cancelled , completed, and cars not available.

• Below diagram mentions that how many orders are cancelled because of driver not present.

• Below diagram represent the number of request are appeared on particular date

Below diagram represent how many request id are from each pickup point.

Based on the provided visualization:

1. First we know that there are 300 drivers were present
2. From airport 1327 trips are completed in city1504 trips are completed
3. From airport 1713 are not completed beacause car not available and city 937
4. cancelled at airport is 198 and in city it is 1066
5. around 2650 rides cancelled beacause driver not available
6. 2016 july 15 there are requests are appeared when compare to whole data.
7. City has more request of rides

Conclusion:

Airport: In airport there are total 3238 requests in that

• 1713 are cars not available
• 198 cancelled
• Here we get that more number of trips are cancelled due to cars not available at airport SEND SOME MORE CARS TO AIRPORT.

CITY: In city thee are total 3507 requests in that

• 1504 Trip completed
• 1066 cancelled
• 937 no cars available
• Need to improve the service in city because more number of people are cancelled the uber.