Data analysis and visualization of UBER
- 1. Descriptive Analysis and Visualization UBER TECHNOLOGIES INC Harshini Nivetha Rajaram
- 2. Visualization Date Eyeballs Zeroes Completed Requests Unique Drivers 1 feb - 3 feb 3313 1112 1964 2485 950 4 feb - 6 feb 3745 1529 2410 2482 1155 7 feb - 9 feb 3666 1324 2490 2552 1136 10 feb - 12 feb 3583 1270 2279 2601 1231 13 feb - 15 feb 2843 1077 1708 2108 759
- 3. Completed v/s Requested : While comparing requested completed tours with approved tours, it is easy to conclude that for many reasons all requested tours have not been completed. To order to solve this problem business, the often encountered problem for these many trips must be investigated and defined. Eyeballs v/s Unique Driver v/s Zeroes:
- 4. Even with the high active users due to lower cab services, higher zero numbers lead when contrasting eyeballs and specific drivers with none. The issue has been found from the side of the company. The organization will minimize these nulls to a minimum level with the correct use of resources. DATA ANALYSIS Time (Local) EYEBALLS ZEROES Mean 12 Mean 51 Mean 19 Standard Error 0 Standard Error 2 Standard Error 1 Median 12 Median 53 Median 19 Mode 7 Mode 83 Mode 7 Standard Deviation 7 Standard Deviation 30 Standard Deviation 11 Sample Variance 48 Sample Variance 877 Sample Variance 130 Kurtosis -1 Kurtosis -1 Kurtosis -1 Skewness 0 Skewness 0 Skewness 0 Range 23 Range 100 Range 40 Minimum 0 Minimum 1 Minimum 0 Maximum 23 Maximum 101 Maximum 40 Sum 3864 Sum 17150 Sum 6312 Count 336 Count 336 Count 336 COMPLETED TRIPS REQUESTS UNIQUE DRIVERS Mean 32 Mean 36 Mean 16 Standard Error 1 Standard Error 1 Standard Error 1 Median 31 Median 38 Median 15 Mode 11 Mode 45 Mode 14 Standard Deviation 20 Standard Deviation 21 Standard Deviation 9 Sample Variance 404 Sample Variance 461 Sample Variance 88 Kurtosis -1 Kurtosis -1 Kurtosis -1 Skewness 0 Skewness 0 Skewness 0 Range 69 Range 73 Range 31 Minimum 0 Minimum 0 Minimum 0 Maximum 69 Maximum 73 Maximum 31 Sum 10851 Sum 12228 Sum 5231 Count 336 Count 336 Count 336
- 5. Descriptive Analysis The total data point count is 336. The date range of data set was FEB 1 to FEB 15. TIME : Average ETA is 12 min So that the company need to reduce average waiting time to retain customers. The max ETA in the case is 23 min. An investigation must be done for why it is be done. The standard deviation for mean ETA of 12min is 7 min. EYEBALLS : Average Active Users is 51 per hour The max active users in this case is 101. The Minimum active users in this case is 1, and the standard deviation for average active user of 51 is 30 users. Implement strategies so that to increase the minimum active users to a higher count. ZEROES : Average Unavailability is 19 users per hour The maximum Unavailability that occurs is 40 users per hour. The minimum unavailability of service per hour is for 0 users. The concern of the company is to decrease maximum service unavailability occurrence to a least number. COMPLETED TRIPS : Average Completed trip count is 32 per hour. The maximum Completion is 69 trips. The minimum Completion is 0 trips and company should plan and work so that to increase the trip completion to a maximum.
- 6. REQUESTS : Average trip requests are 36. Maximum trip request is 73 and Minimum trip request is 0. The company should investigate and plan accordingly to increase the minimum trip request to business growth as well. UNIQUE DRIVERS : Average number of unique drivers are 16 per hour. Maximum Unique driver is 31 and minimum unique driver is zero. This should be a concern for the firm even when there are lot of active users available and the available drivers becomes zero will affect the firm. So that the firm should invest and should do the needful to increase the minimum unique drivers’ availability.