A classic regression problem solved with supervised machine learning.
Ark Bike is a ride sharing platform where customers can rent bikes on an hourly or daily basis. The goal of this project is to use machine learning and regression and develop an effective model that can predict the number of bikes that will be needed in any given time period, using historical data. This data is highly correlated to environmental and seasonal settings like weather conditions, precipitation, day of week, season, hour of the day, etc. which can affect the rental behavior. I will be applying different regression techniques (multi-linear regression) using the existing libraries in python. This project would also discuss some of the hypothesis that could influence the demand of the bikes. The proposed system involves a multilayer perceptron network with a feed forward and a back-propagation learning algorithm to meet the necessary outcome. Training data will be used to train the model and test data will be used to the predict the outcome. The Significance of this project is to help the business which oversee such kind of an observation and this also helps the bike manufacturing companies to have an idea on the production of bikes over four different seasons in a year.
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Hi, i hope you are doing well. I have 5 years of experience in machine learning and python. You can check my account that I only do machine learning projects. I hope you will satisfy with my service.
hi i can get this job done for you in the required time. Would like to have a discussion with you. my github: [login to view URL] my kaggle account: [login to view URL]