Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.

Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.

Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA. We aggregated the data on daily basis and then extracted and added the corresponding weather and seasonal information.

• season : season (1:springer, 2:summer, 3:fall, 4:winter)
• yr : year (0: 2011, 1:2012)
• mnth : month ( 1 to 12)
• holiday : weather day is holiday or not
• weekday : day of the week
• workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
• weathersit :

1. Clear, Few clouds, Partly cloudy, Partly cloudy
2. Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
3. Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
4. Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog

• temp : Normalized temperature in Celsius. The values are divided to 41 (max)
• atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)
• hum: Normalized humidity. The values are divided to 100 (max)
• windspeed: Normalized wind speed. The values are divided to 67 (max)
• cnt: count of total rental bikes including both casual and registered

Create a program that uses Batch Gradient Descent Algorithm to predict the number (count) of total rental bikes in a given day.

Separate the dataset randomly into 70% Training set and 30% testing set. And calculate the performance of your algorithms with three different learning rates and three different numbers of epochs.

the file : bikes.csv i will try to put it in commant pleas help shortly