prompt for regression

I want to develop a generic multiregression program. Each ML regression method must be in a function. Ensure logging library is used. The file name for log should be log_timestamp.log. All codes should be in try except block so that if multiple ML regression are executing, the error on one should not stop the other from executing. The code should define a variable k_fold. If k_fold is 10 then its 10 fold validation, if it is 7 then seven fold validation. The code should define a variable dataset_percent. If dataset_percent is 10 then use 10 percent of the data and so on. Store a csv for each algo in the output folder. the file name should be the ml algo name. The csv in the output folder will have two columns : 1st column has a header called actual, 2nd column has a header called predicted. Results of all fold should be exported to csv. Since its a k fold code, for a given algo append the output generated k times in a single file for a single algo. The input file is input.csv stored in the input folder. Also do that min-max scaler for all column and later on inverse it too while storing the output with actual and predicted values. Please also display the the time to train, time to test, and total time of execution of each ML algo. The regression results should append in the subsequent runs in the csv file called time.csv which should be in append mode. Assume last column as the one to be classified.

The algos are:

1. Linear Regression
2. Polynomial Regression
3. Ridge Regression
4. Lasso Regression
5. Elastic Net Regression
6. Bayesian Ridge Regression
7. Ordinary Least Squares Regression (OLS)
8. Huber Regression
9. Theil-Sen Estimator
10. Quantile Regression
11. Decision Tree Regression
12. Random Forest Regression
13. Gradient Boosting Regression
14. XGBoost Regression
15. LightGBM Regression
16. CatBoost Regression
17. Support Vector Regression (SVR)
18. K-Nearest Neighbors Regression (KNNR)
19. Principal Component Regression (PCR)
20. Partial Least Squares Regression (PLSR)
21. Artificial Neural Networks (ANN) Regression
22. Multi-layer Perceptron (MLP) Regression
23. Stochastic Gradient Descent (SGD) Regression
24. Bayesian Regression