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
  2. Ridge
  3. Lasso
  4. ElasticNet
  5. K-Nearest Neighbors (KNN)
  6. Support Vector Machine (SVM)
  7. Decision Tree
  8. Bagged Trees
  9. Extra Trees
  10. Random Forest
  11. AdaBoost
  12. Gradient Boost
  13. XGBoost

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
  4. Kaggle Medical Cost Personal Datasets  https://www.kaggle.com/mirichoi0218/insurance