1. Introduction.      

1.1. Introduction

1.2. Overview of the ten-step methodology

2. Data Preprocessing:      

2.1. Introduction

2.2. Data Preprocessing

2.3. Download the original data

3. Dataset split   

3.1. Introduction

3.2. Polynomial Features

3. 3. Dataset split

4. Linear Regression 

4.1. Introduction

4.2. Training the models

4.3. Generating the predictions

4.4. Test errors

4.5. Training errors

4.6. Overfitting analysis

4.7. Conducting the naive test

4.8. Sensitivity analysis and hyperparameter tuning

4.9. Sensitivity analysis

4.10. Forecasts: theory and methodology

4.11. Producing the forecasts

4.12. Finding test predictions using the mathematical formula

4.13. Final selection of models.

5. Conclusions

5.1. Conclusions