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# Untitled

a guest Jun 25th, 2019 64 Never
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1. from sklearn.linear_model import LinearRegression
2. from sklearn.preprocessing import StandardScaler
3. from sklearn.preprocessing import PolynomialFeatures
4. import re
5.
6. cols_2012 = crime_realEstate.filter(regex='_2012').columns
7. crime_realEstate['Area_Name']=crime_realEstate['Area_Name'].apply(lambda x: re.sub(' ', '_', str(x)))
8. regDF_2012 = crime_realEstate[cols_2012]
9. regDF_2012 = regDF_2012.assign(community_code=crime_finalDF['community_area'])
10. regDF_2012.dropna(inplace=True)
11. X_feats = regDF_2012.drop(['Avg_Price_2012'], axis=1)
12. y_label = regDF_2012['Avg_Price_2012'].values
13.
14. from sklearn.preprocessing import scale
15. from sklearn.decomposition import PCA
16. from sklearn.model_selection import KFold
17. from sklearn.model_selection import cross_val_score
18. import numpy as np
19.
20. pca = PCA()
21. pca_ = pca.fit_transform(scale(regDF_2012))
22.
23. np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)
24.
25. n = len(pca_)
26. kf_10 = KFold(n, n_folds=10, shuffle=True, random_state=0)
27. # kf_10 = KFold(n, shuffle=True, random_state=2)
28. regr = LinearRegression()
29. mse = []
30.
31. score = -1*cross_val_score(regr, np.ones((n,1)), y.ravel(), cv=kf_10, scoring='mean_squared_error').mean()
32. mse.append(score)
33.
34. for i in np.arange(1,6):
35.     score = -1*cross_val_score(regr, X_reduced[:,:i], y.ravel(), cv=kf_10, scoring='mean_squared_error').mean()
36.     mse.append(score)
37.
38. fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,5))
39. ax1.plot(mse, '-v')
40. ax2.plot([1,2,3,4,5], mse[1:6], '-v')
41. ax2.set_title('Intercept excluded from plot')
42.
43. for ax in fig.axes:
44.     ax.set_xlabel('Number of principal components in regression')
45.     ax.set_ylabel('MSE')
46.     ax.set_xlim((-0.2,5.2))
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