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analyzing taxi trip data of New York city

$30-250 USD

已取消
已发布将近 8 年前

$30-250 USD

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The taxi trip data should be analyzed. Temporal and geospatial data should be analyzed. The project is to investigate the relationship between the borough in which a driver drops his passenger off and the amount of time it takes to acquire another fare. The main aim is to build an application that could recommend the best place for a cab to go after a dropoff, based on the current traffic patterns and the historical record of next-best locations that is contained within this data. You could also look at the information from the perspective of someone trying to catch a cab: given the current time, place, and weather data, what is the probability that I will be able to hail a cab from the street within the next five minutes? This sort of information could be incorporated into applications like Google Maps to help travelers decide when to leave and which travel option they should take. The code is available at [login to view URL] In the following uploaded file chapter 8 can be used for reference.
项目 ID: 10776564

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活跃8 年前

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Hi, Below is my experience, and I can deliver you quality work with in specified or agreed time. Myself Ph.D. in advanced analytics having 10+ years of experience in developing and delivering analytical projects using open source Spark/R (including in-memory computing), and can deliver your requirement accompanied by a step-by-step word document, what each line or code means. As, freelancer.com bid does not allow for attaching; cannot showcase my expertise in this area. I am available at freelance chat (click on my name and options), to understand the requirements and explain my approach if required in detail. Waiting for your reply (or) initiation for chat discussion. Regards, Dr. Kumar PM.
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Previously, I have developed a Recommendation system using Hadoop that will look into the customer buying pattern and will recommend the product to them that will increase the sales. I'm familiar in Machine Learning algorithms specially in unsupervised learning.
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会员自6月 14, 2016起

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