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预测分析课业代写 Predictive Analytics代写 作业代写

ISE 529 Predictive Analytics

Homework 6

预测分析课业代写 Submit a report on a pdf with white background. 1.Use the Boston dataset from sklearn.datasets to fit the following models to predict the price of houses in the Boston area. Read boston.data into a DataFrame using

Submit a report on a pdf with white background.

1. 预测分析课业代写

Use the Boston dataset from sklearn.datasets to fit the following models to predict the price of houses in the Boston area. Read boston.data into a DataFrame using

X = pd.DataFrame(boston.data,columns = boston.feature_names).

Before modeling let us rename some features. Rename features RM and CHAS with

X.rename(columns = {’RM’:’MR’,’CHAS’:’HAS’},inplace=True). Then use X.columns.str[:1]

to rename all 13 columns by their first letter. Whenever needed (use random_state = 0 and default test, train sizes). Use sklearn to fit all models.

a) (10 pts.)

Use MinMaxScaler() to scale all features in (0, 1). Split the data into train and test set .

Fit a linear regression model. Report test R2 , test MSPE.

b) (15 pts.) 预测分析课业代写

Use poly = PolynomialFeatures() to add 93 features in a new array X3.

Use poly.get_feature_names(X.columns) to review the names of the old and new columns.

Convert array X3 to a DataFrame using

X3 = pd.DataFrame(X3,columns=poly.get_feature_names(X.columns))

We will call X3 the extended Boston dataset.

Use MinMaxScaler() to scale all 104 features in (0, 1), call it X4.

Split X4 into train and test set. Use these sets for all of the following parts in this homework.

Fit a linear regression model. Report test R2 , test MSPE.

c) (15 pts.) 预测分析课业代写

Use Ridge(alpha = 0.1,normalize = True).fit(X_train,y_train) to fit a RR model.

Notice that we are normalzing the already scaled data in X4. Report test R2 , test MSPE.

d) (15 pts.)

Search for the best alpha value then fit the RR model again. Report test R2 , test MSPE.

e) (15 pts.)

Fit a Random Forest model on 500 trees with max_features = 10, max_depth = 6, random_state=0. Report test R2 , test MSPE.

f) (15 pts.)

Find most important features in the extended Boston dataset identified by the RF. Report the top seven. What original features are most important?

g) (15 pts.)

Use GridSearchCV to find best values for max_features, max_depth. Fit the RF with these values and report test R2 , test MSPE.

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