Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes.

Attribute Information:

Date : year-month-day Rented Bike count - Count of bikes rented at each hour Hour - Hour of he day Temperature-Temperature in Celsius Humidity - % Windspeed - m/s Visibility - 10m Dew point temperature - Celsius Solar radiation - MJ/m2 Rainfall - mm Snowfall - cm Seasons - Winter, Spring, Summer, Autumn Holiday - Holiday/No holiday Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)

The exploratory analysis of the data including descriptives that may suggest a possible model that is adequate for fitting the data. Do the data show a non- linear relationship? Should a transformation of the response variable and/or the predictors be useful?

Try interaction variables.

Use either liner, logistic, polynomial regression techniques and/or

transformations

Check for collinearity among the independent variables.

A variable selection method will enable you to select suitable models and find

the set of predictor variables, which are more informative for predicting the

response variable.

You may want to fit a few models that seem adequate for your data and then

select the model among them that provides the ``best'' prediction of Y.

Analyze the residual plots to look for patterns that might suggest a failure in

the assumptions and some inadequacies in the selected model.

The existence of outliers and influential points may have dramatic effects on

your analysis. Check also if there are outliers.

Can your model be improved? Are you satisfied with the model you have

chosen?

Usetheselectedregressionmodeltoexaminetherelationshipand

associations among the variables in your study and to identify, among the observed independent variables, the strongest predictors for the response variable.

Computetwopredictionsincludingthepredictionintervalsusingthe regression model.

Applyvalidationtechniquestoevaluatethepredictivepowerofyourmodel. Split the original dataset at random into a training and test set. Test set should have at least 15 observations in order to compute meaningful validation statistics. Discuss the model performance using training, and testing sets.