adding all work done so far (lessons 1 - 5)

python/Supervised Learning/Linear Regression/25. Polynomial Regression/solution.py

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+# TODO: Add import statements
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+
+# Assign the data to predictor and outcome variables
+# TODO: Load the data
+train_data = pd.read_csv('data.csv')
+X = train_data['Var_X'].values.reshape(-1, 1)
+y = train_data['Var_Y'].values
+
+# Create polynomial features
+# TODO: Create a PolynomialFeatures object, then fit and transform the
+# predictor feature
+poly_feat = PolynomialFeatures(degree = 4)
+X_poly = poly_feat.fit_transform(X)
+
+# Make and fit the polynomial regression model
+# TODO: Create a LinearRegression object and fit it to the polynomial predictor
+# features
+poly_model = LinearRegression(fit_intercept = False).fit(X_poly, y)