Project description Citi Bike Sharing System Dataset Analysis in NYC The growth of Bikes sharing system usage in some popular cities has been rising during the past years. The benefits of bikes in urban settings, where the distances are short, and the parking prices are high, has caused the demand for such systems to increase. Managing this system and understanding its data will be helpful in decision-making process. The purpose of analyzing the dataset of New York citibike sharing system is to find some trips patterns and answers some questions such as where do bikers ride? when do they ride? how far do they go? which station are most popular? what days of the week are most rides taken on? The answers of these questions will be used to enhance the company services.The analysis process include preparing data, cleaning data.and After analyzing the data, I will visualize the findings through Tableau, and MapGIS for interactive maps. Another part of the project will be a prediction model for the availability of bikes or ducks in each station at specific time in the future. The result of the prediction will help the stakeholders to enhance their services. For example, if I found that in the Summer, the demand for the bikes will be increased at certain area. So the company can provide more bikes or increase the station’s capacity of that area. I will build the prediction model wit Python using clustering and classifying techniques. The dataset is provided by citibike Sharing System