Capstone Project Notes - 2 1

Capstone Project Notes - 2

1. Model building and interpretation.

a. Build various models (You can choose to build models for either or all of descriptive, predictive or prescriptive purposes).

b. Test your predictive model against the test set using various appropriate performance metrics.

c. Interpretation of the model(s).

2. Model Tuning

a. Ensemble modelling, wherever applicable.

b. Any other model tuning measures.

c. Interpretation of the most optimum model and its implication on the business (explanatory power of a variable).

d. Insights and Business Recommendations.

1. Regression Models - we may choose any of the below listed models for model building

1. Linear Regression
2. Random Forest
3. Neural Network
4. Bagging
5. Boosting
6. KNN

2. Tuning the Models (Hyper Parameter Tuning using Grid Search CV)
3. Metrics (R2, Adjusted R2, RMSE, MAPE)
4. Create a chart in an excel/table format to compare the Train & Test for all the models created.
5. Create a chat in an excel/table format to compare the data between before Training and after Training
6. Best Model Selection
7. Explanatory power of a variable
8. Insights and Recommendations on Models finding.