In this module I analyzed the Citibike NYC Bikesharing data to make a case to convince investors that bike sharing business in Des Moines is a lucrative proposition.
In this module I am creating a story with data and its analysis to help the stakeholders to decide whether to invest or not. I am using Tableau as a tool for this project. Before analyzing the data, I asked a few questions and to answer these questions I am analyzing and portraying the data:
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What are the total number of trips?
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If the customers are for short term or Annual subscribers?
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Which are the peak months, the days of the week and the hour in day?
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Which are the top locations for starting and ending the bike rides?
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What is the popularity of bike sharing based on gender and age?
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How many numbers of bike would be utilized for how much time and then how many bikes would need to be repaired and how often?
Jupyter Notebook and Tableau Public
[link to dashboard](LINK GOES HERE "https://public.tableau.com/app/profile/ritu7695/viz/challenge_16633087617930/Story1)
This graph is showing the length of time for which bikes were checked out for all riders, and the insights it gives is that the bikes were checked out for one hour mostly and beyond the 60 minutes the curve is completely flat. The highest number of checked out bikes were for first 20 minutes and then it goes down.
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The profile for this graph is similar to the previous one, but this graph gives us more information about the checked-out time based on the gender. This graph shows males have checked out the bikes more in comparison to other genders.
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This is a heat map which shows the number of bike trips on weekdays for each hour of the day. This graph gives us the insight that the hours between 6 AM to 9 PM are busier than the rest of the hours, and also shows on the weekdays certain window of hours busier in comparison.
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This graph is more elaborate than the previous as. This heatmap gives information about the number of trips on weekdays and in each hour. It shows that the males are more renting the bikes in comparison to other genders which again in line with our earlier observation.
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This visualization is a heat map that shows the “usertype” of the customer with details of their gender type who has taken bike trips on each weekday. This graph shows that the annual subscriber and males have taken the more bikes’ trips relative to the daily customers.
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6. Below is an image of the dashboard, it consists of the graphs for “Trip by age”, “gender breakdown”, “bike utilization”, “bike repairs”, “top starting location” and “ending locations”. All these graphs giving a great insight into the data.
Trip by age graph, is showing that the younger people are more consistently sharing the bike. Gender breakdown graph is showing that males are more likely to rent bike in comparison to other genders. Bike repairs graph, is giving information on how many bikes would need most maintenance so that we can understand the cost of bike upkeep. Bike utilization visualization gives information on about how long these bike rides were and therefore might need more attention than others. Top starting and ending points showed the locations from where bikes are rented most frequently.
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With this project was able to analyze and gained insights from Bikesharing data as a proof of concept to start a similar business in another city. With the given data I could identify the months, weekdays, and hours of the day which are busy and which starting and ending locations for bike riding are popular. The analysis of data provided us many useful insights to start with the idea for setting up the bike sharing business. However, we need to put more efforts into the research to make this business into a successful endeavor in another city. A successful business in New York doesn’t guarantee a success in another city. It would be necessary to carry out to do some analysis on traffic behavior, population in different age groups who are interested in bike riding and sharing. How favorable are the weather conditions of the city in relation to bike riding. It would be good to know if the people are interested in owning a bicycle or renting it. New York is big and expensive city where storing a bike at home could be an issue, so people might be more interested in renting it rather than owning it. What is the objective of people who are interested in bike sharing and riding. Is it for leisure/health fitness or for commuting? With the present insights from this exercise, I can’t make any decision if the bike sharing business in Des Moines would be as profitable as in NYC.