Building the Model

Explore the process of creating a regression model with PyCaret by training a Gradient Boosting Regressor, tuning hyperparameters, evaluating using k-fold cross-validation, and visualizing model performance. Understand how to finalize and save models for deployment.

We'll cover the following...

Creating the model
Tuning the model
Making predictions
Plotting the model
Finalizing and saving the model
Implementation

Create model

	MAE	MSE	RMSE	R2	RMSLE	MAPE
0	2324.8131	24607040.5795	4960.5484	0.8393	0.3810	0.1751
1	2571.2202	29114809.8137	5395.8141	0.7413	0.4376	0.2551
2	2483.0865	21498482.3051	4636.6456	0.8585	0.4261	0.2000
3	2545.2911	25489822.2086	5048.7446	0.8158	0.3937	0.1765
4	2218.7259	19926308.9440	4463.8894	0.8240	0.4237	0.2161
5	1991.9530	17546103.1066	4188.8069	0.9050	0.2883	0.1446
6	2557.6356	30138770.5115	5489.8789	0.8316	0.4120	0.1911
7	1904.3245	16102540.3261	4012.7971	0.8950	0.3101	0.1860
8	1789.5416	17459155.1593	4178.4154	0.8269	0.3299	0.1574
9	2368.0500	26271250.1647	5125.5488	0.8125	0.4560	0.1721
Mean	2275.4641	22815428.3119	4750.1089	0.8350	0.3858	0.1874
SD	274.1307	4766740.8468	501.8900	0.0435	0.0546	0.0297

1.Introduction to Machine Learning

2.Regression

3.Classification

4.Clustering

Project

5.Anomaly Detection

6.Natural Language Processing

7.Deploying a Machine Learning Model

8.Conclusion

9.Appendix

Building the Model

Creating the model

Create model