Bike count prediction using Linear Regression - Swati Kaiwar

February 23, 2019
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