Bike count prediction using Linear Regression - Swati Kaiwar
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