This project is for predicting the bike count using Linear Regression.
This experiment is for carrying out Machine Learning Life Cycle using the Algorithm, Linear 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 "Linear Regression" to the Train Model 7. Score Model 8. Evaluate Model After running the experiment, below are the results: Mean Absolute Error 80.505951 Root Mean Squared Error 111.198382 Relative Absolute Error 0.56425 Relative Squared Error 0.375017 Coefficient of Determination 0.624983