Mini project on Uber data analyzis and visualization.

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.