Exercises

Practice building and evaluating machine learning models in scikit-learn with the following exercises:

1. Build a clustering model to distinguish between red and white wine by their chemical properties:

   a) Combine the red and white wine datasets (data/winequality-red.csv and data/winequality-white.csv, respectively) and add a column for the kind of wine (red or white).

   b) Perform some initial EDA.

   c) Build and fit a pipeline that scales the data and then uses k-means clustering to make two clusters. Be sure not to use the quality column.

   d) Use the Fowlkes-Mallows Index (the fowlkes_mallows_score() function is in sklearn.metrics) to evaluate how well k-means is able to make the distinction between red and white wine.

   e) Find the center of each cluster.

2. Predict star temperature:

   a) Using the data/stars.csv file, perform some initial EDA and then build a linear regression model of all the numeric columns to predict the temperature of the star.

   b) Train the model on 75% of...