• Data set from UCI Machine Learning Repository.
• It specifically focuses on forecasting the bike rental demand in the capital bikeshare program combining historical usage pattern with weather data.
• Then I Carried out series of exploration on the data set specifically a correlation heat map which showed the relationship between the weather data giving an indication of which attributes to drop during Modelling.
• Using a linear regression model from sklearn we got a mean squared error(MSE) of 0.221 which showed that the values are closer to the regression line and a regression score of 0.964
• The forecast model showed that the bike rental demand is on the high side.

Link to Github Repository