Implementation

[Sukhpreet1987]

Week 3

Bike share demand data set has total 12 variables in train data set whereas 9 variables in test data set as discussed methodology. In the beginning of predictive model implementation,I tried to look for variables those are carrying maximum information.

[Sukhpreet1987]

Getting good patterns from historic data is challenging task in moving vehicles at different locations and this can be achieved with different statistical as well as some data mining models.

I aim to present a modeling approach to the problem, which has different data mining models such as Random Forest ,Neural networks ,PCA( Principal Component Analysis) and Conditional trees to predict the user counts which have registered and casual users who travel by bike sharing bicycles.

Hence I have started our model implementation with principle component analysis.