Project 3

Create a small Flask App that captures user input and generates a prediction.

TASK

1. Choose from the following three datasets:

• A - Used Cars ~ Predict Price (convert to CAD)
• B - Abalone ~ Predict Age (Rings)
• C - Tips ~ Predict Tip ($)

2. Build a model on top of this data
3. Save the model 🥒
4. Wrap your saved model in a small Flask wrapper
5. Have users input different X values to generate new predictions

RUBRIC

Your project (model and Flask App) must:

• establish a benchmark and a naive model

• use a LinearRegression, 1 of Lasso/Ridge/ElasticNet, and 1 CatBoostRegressor/XGBoostRegressor

• have evidence of grid searching

• use sklearn.pipeline

• accept user input and be able to generate new predictions on the fly

• use a 3rd-party python library/package that we haven't discussed.

OPTIONAL

If you've crushed the required bits with time to spare:

• add a more pretty interface to your model/flask app using HTML/CSS
• host the app on heroku (or something similar)