As we can refer the above bar plot: The Birth Year and Gende column has missing values. Around 15% values are missing in both the columns.As the missing percentage is not very huge, I decided to provide random values 'Male,Female' in the gender column. For the birth year column I decided to remove the null records
The ratio between Male and Female is around 75:25. Which means Divvy has more number of Male customers than Female.
The above plot clearly dipicts that Divvy has around 90% of its user as subscribers and rest of the users are customers. I believe more number of Divvy Bike users are working population who rents the bike on a daily basis. Thats the reason it has more number of subscribers than the customers.Divvy's subcription plan seems to be reasonable for the customers. Divvy should keep focusing on maintaining its subcribers
After futher breaking down the user type into gender we can see that there are more male subscribers than the female subscribers.
The average trip duration in FY 2018 is 952 seconds or 16 mins. This is also a clear indication that Divvy users are mostly working population who rents the bike to cover the distance after getting down from bus or train.
The above bar plot is showing the bike distribution acrross the week days. Wednesday being on the highest bar we can say that Wesdnesday is the peak day people renting Divvy Bike. The plot clearly indicates that on Saturday and Sunday less number of users are owing the bike in comparison to the week days.
The above line chart is showing the variations in number of bikes owned across the four quarters. The useful insights from this graph is as below: - Q3 has 3 times more users than Q1. The reason behind this may be that winters in Chicago are worse, specially in the month of Jan and Feb. On the opposite side the summers of Chicago (July and August) is very beautiful, that is the reason people prefer to commute to the work in the summer renting a bike.
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- In Q2 there is a increase in users are compare to Q1, because Q2 includes the month of May and June, which are pretty warm and nice. - There is a sharp decrease in the users from Q3 to Q4, beacause the winters in Chicago are the worst.Huh!!
The above bar plot is showing the breakdown in months, which is giving a clear indication of the winters in Chicago. July and August being on the highest bar and december, jan and Feb being on the lowest bar.
Technical description: For creating the above histogram, first we created a new column of Age from the Birth year column. Then we divided the age into 10 bins. Bins = 10,15,20, 25,30,40, 50,60,70,80, 100 From the statistical point of view, the histogram is showing a normal distribution.The histogram is having more data points lying in the bins of 20 to 40.
Business Value: The above histogram seems very important for the business purpose. As we can see most of the users are lying in the age of 30 to 40, Divvy stakeholders can make various strategy to focus on the customers aged more than 40 years. They should focus on why people above 40 are not renting the Divvy Bikes??
In the above horizontal bar plot we have plotted 10 most frquent starting points in the data. The above ten starting point, I think would be near to a bus stop or a train station.
Business Insight: Divvy should make sure that bikes are always available to pick up on these points.
In the above horizontal bar plot we have plotted 10 most frquent ending points in the data. The above ten ending point, I think would be near to some business community or business street
Finally these are the 10 frequent routes in the data. This plot can be very helpful for Divvy stakeholders in taking various marketing decisions:
The above study was very helpful for us to understand various business aspect of Divvy Bikes.
Most of the users are lying in the age of 30 to 40, Divvy stakeholders can make various strategy to focus on the customers aged more than 40 years. They should focus on why people above 40 are not renting the Divvy Bikes??