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

python/Supervised Learning/Linear Regression/25. Polynomial Regression/data.csv

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+Var_X,Var_Y
+-0.33532,6.66854
+0.02160,3.86398
+-1.19438,5.16161
+-0.65046,8.43823
+-0.28001,5.57201
+1.93258,-11.13270
+1.22620,-5.31226
+0.74727,-4.63725
+3.32853,3.80650
+2.87457,-6.06084
+-1.48662,7.22328
+0.37629,2.38887
+1.43918,-7.13415
+0.24183,2.00412
+-2.79140,4.29794
+1.08176,-5.86553
+2.81555,-5.20711
+0.54924,-3.52863
+2.36449,-10.16202
+-1.01925,5.31123

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()
+

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)