Objectives and Goals:
1. By using software R or some data mining techniques, Analyze the open data of Bixi Bikes from
their online site of year 2014, 2015 and 2016, resulting in prediction for year 2017.
2. Need a report of 25 to 30 pages.
3. Compare them and analyze which months are very demanding for BIXI and also compare them.
Categories them as the busiest and less busy months.
4. Also, compare the summer time (May, June, July and August) and define tourism and vacation
time and how it can benefit Montreal.
5. Check where are more users for bikes and the need to put more bikes in the docks. For example,
downtown areas, near universities, Tourism areas, etc.
6. Describes about how Canada’s weather effects all the facility of BIXI bikes to the users.
7. What can we do to use more BIXI bikes so that it will help environment as well? For instances,
offers, discounts, stylish bikes that been recently used by the BIXI people.
8. Make clusters, line graphs, histograms, tables, diagrams, figures, etc.
9. At the end, Benchmark the Montreal’s BIXI system with the Toronto’s or Vancouver’s BIXI
system. Some basic fundamentals that can be used, for example, users, number of bikes being
used, facilities, modes of payment, availability, etc.
Décerné à :
21 freelance font une offre moyenne de $304 pour ce travail
I am a data scientist and have experience with machine learning and statistical analysis of data using R and Python. I would like to help you with your project. Please provide me more details and dataset.
I am professional freelancer having MBA with over 11 years of experience in Financial Field. I can complete your task effectively and efficiently in a timely manner.
We are a team of Data scientists having an expertise in R. Would like to explore these data and add value to your Business. Looking forward to work with you. Best regards, Team DataStrats!
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