End-to-end ML modelling process for regression problem
  1. Data reading and preprocessing: missing data, visualizing, scaling
  2. Algorithm selection
  3. Model Evaluation: train-test split, k-fold cross validation, metrics (mae,mse,rmse,r2-score)
  4. Hyperparameter tuning: grid search
  5. Final model Saving into disk and loading

Algorithms
  1. Linear Regression (Ordinary Least Squares; OLS)
  2. Partial Least Square Regression (PLS)
  3. Ridge 
  4. Lasso 
  5. Elastic Net
  6. K-Nearest Neighbors (KNN)
  7. Support Vector Machine (SVM)
  8. Bagged Multivariate Adaptive Regression Splines (MARS)
  9. Decision Tree (Classification & Regression Trees; CART)
  10. Bagged CART
  11. Random Forest
  12. Stochastic Gradient Boosting
  13. Neural Network 

Examples
  1. StatLib Calfornia Housing Price data  http://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html
  2. UCI Boston House Price data  https://archive.ics.uci.edu/ml/machine-learning-databases/housing/	
  3. Hyundai Heavy Industry Cruise Ship data