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

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

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+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+sns.set()
+
+df = pd.read_csv('data.csv')
+# print(df)
+X = df[['Var_X']]
+y = df[['Var_Y']]
+
+poly_feat = PolynomialFeatures(degree=2)
+
+X_poly = poly_feat.fit_transform(X)
+
+poly_model = LinearRegression(fit_intercept=False).fit(X_poly, y)
+print(poly_model)
+# sns.lineplot(x='Var_X', y='Var_Y', data=df)
+# plt.show()
+