Dummy Classifier Workshop Thing

import pickle
import os
from sklearn.model_selection import train_test_split
from sklearn.dummy import DummyRegressor
import seaborn as sns

# Set a random seed so that we can see the difference between classifiers
random_seed = 1

# Load data from the internet, or from cache if available on disk
if not os.path.isfile("regression_data.pkl"):
    df_mpg = sns.load_dataset('mpg')
    df_mpg = df_mpg.dropna()
    df_mpg = df_mpg.drop('origin', axis=1)
    df_mpg = df_mpg.drop('name', axis=1)

    X = df_mpg.drop('mpg', axis=1)
    y = df_mpg['mpg']

    pickle.dump((X, y), open("regression_data.pkl", "wb"))
else:
    print("Loading data...")
    X, y = pickle.load(open("regression_data.pkl", "rb"))

# Separate train/test data from original data
print("Splitting test and train data...")
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=random_seed)

# Create your classifier
classifier = DummyRegressor()

# Fit the classifier
print("Fitting the classifier...")
classifier.fit(X_train, y_train)

print("Training set score: %f" % classifier.score(X_train, y_train))
print("Test set score: %f" % classifier.score(X_test, y_test))