This project is for predicting the bike count using Boosted Decision Tree Regression.
This experiment is for carrying out Machine Learning Life Cycle using Algorithm, Boosted Decision Tree Regression. Steps: 1. Dataset: Bike Rental UCI dataset 2. Select Columns in Dataset season,mnth,hr,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,cnt Above columns are Categorical and will affect the bike rental count. 3. Edit Metadata Select Categorical="Make Categorical" 4. Filter Based Feature Selection Select "Chi Squared" and select "Cnt" i.e. Number of desired features=1 5. Split Data Splitting Mode="Split Rows", Fraction of the rows=0.7 (to send 70% of the data to the Train Model) 6. Train Model Connect "Boosted Decision Tree Regression" to the Train Model 7. Score Model 8. Evaluate Model After running the experiment, below are the results: Mean Absolute Error 49.276969 Root Mean Squared Error 71.945755 Relative Absolute Error 0.345372 Relative Squared Error 0.156987 Coefficient of Determination 0.843013 Results depict that using the Boosted Decision Tree Regression is a better option than Linear Regression for this dataset because of the COD value here.