The objective of this Case is prediction of bike rental count on daily basis based on the environmental and seasonal settings.
Our task is to build a regression model that will predict the bike rental on daily basis based on different environmental and seasonal settings. Given below is a sample dataset which we are using to predict the bike rental count.The varibale names are rather self explanatory.
Distribution of "cnt" - The count of daily bikes rented
Distribution of Number of Bikes-Rented by Month
Distribution of Number of Bikes-Rented by Season
Plot of "temp" -temperature Vs. "cnt" - The count of bikes rented
Plot of "dteday" - The dates Vs "cnt" - The count of bikes rented
Based on the correlation plot, we remove atemp.
Implemented multiple linear regression with an accuracy of 87%
Implemented Random Forest with an accuracy of 90%