Understood the files which contain Hourly rental data between casual and registered users from year 2011- 2012
train-data.csv- contains first 19 days of data
and test-data.csv - contains data from 20th day to month end
Each file focuses on bike rental trend on the hour.
Training has data set based on registered, casual(non registered), count(registered + casual)
Mobility of bike inside the city can be tracked by the sensor network like functioning of bike sharing system and this gives proper data for predicting the results in good manner.
I will focus on predicting bike demand based on different variables present in the dataset as well as different predictions will be evaluated based on the historical patterns for better management of bike sharing system by getting the user counts in order to keep the bike count in proportion with user counts.
Week 1
Understood the files which contain Hourly rental data between casual and registered users from year 2011- 2012 train-data.csv- contains first 19 days of data and test-data.csv - contains data from 20th day to month end Each file focuses on bike rental trend on the hour. Training has data set based on registered, casual(non registered), count(registered + casual)
Mobility of bike inside the city can be tracked by the sensor network like functioning of bike sharing system and this gives proper data for predicting the results in good manner.
I will focus on predicting bike demand based on different variables present in the dataset as well as different predictions will be evaluated based on the historical patterns for better management of bike sharing system by getting the user counts in order to keep the bike count in proportion with user counts.