Subset Selection Class Assignment

Part 1 SAS: 1) A screen shot of your project window

2) A listing or screen shot of the min, max and mean of the predicted and actual amounts in your data.

3) A listing of the first 15 observations after imputation and prediction.

Part 2: Python 1) A copy of your Python program #Importing Library import pandas as pd import numpy as np from AdvancedAnalytics import ReplaceImputeEncode #Reading Data df = pd.read_excel("diamondswmissing.xlsx") #Missing Values data_map = {\ 'obs': [4,(1,53940)],\ 'Carat':[0,(0.2,5.5)],\ 'cut':[2,('Fair','Good','Premium','Very Good')],\ 'color':[2,('D','E','F','G','H','I','J')],\ 'clarity':[2,('I1','IF','SI1','SI2','VS1','VS2','VVS1','VVS2')],\ 'depth':[0,(40,80)],\ 'table':[0,(40,100)],\ 'x':[0,(0,11)],\ 'y':[0,(0,60)],\ 'z':[0,(0,32)],\ 'price':[0,(300,20000)]\ }

rie = ReplaceImputeEncode(data_map=data_map,display=True) df.rie = rie.fit_transform(df) #Imputing Missing Values from sklearn import preprocessing interval_attributes = ['Carat','depth','table','x','y','z'] interval_data = df.as_matrix(columns = interval_attributes) interval_imputer = preprocessing.Imputer(strategy = 'mean') imputed_interval_data = interval_imputer.fit_transform(interval_data) print("Imputed Interval Data:\n", imputed_interval_data) # Convert String Categorical Attribute to Numbers for further assesment # Mapping of categories to numbers for attribute 'cut' cut_map = {'Ideal':0, 'Premium':1, 'Good':2, 'Very Good':3, 'Fair':4} df['cut'] = df['cut'].map(cut_map) # Mapping of categories to numbers for attribute 'color' color_map = {'E':0,'I':1,'J':2,'H':3,'F':4,'G':5,'D':6} df['color'] = df['color'].map(color_map) # Mapping of categories to numbers for attribute 'clarity' clarity_map = {'SI2':0,'SI':1,'VS1':2,'VS2':3,'VVS2':4,'VVS1':5,'I1':6,'IF':7} df['clarity'] = df['clarity'].map(clarity_map) print(df) # Converting nominal data from the dataframe into a numpy array nominal_attributes = ['cut','color','clarity'] nominal_data = df.as_matrix(columns=nominal_attributes) # Create Imputer for Categorical Data cat_imputer = preprocessing.Imputer(strategy='most_frequent') # Imputing missing values in the Categorical Data imputed_nominal_data = cat_imputer.fit_transform(nominal_data) #inserting imputed data in the data frame df[['cut','color','clarity']] = imputed_nominal_data df[['Carat','depth','table','x','y','z']] = imputed_interval_data df.head() #Encoding scaler = preprocessing.StandardScaler() # Create an instance of StandardScaler() scaler.fit(imputed_interval_data) scaled_interval_data = scaler.transform(imputed_interval_data) print("Imputed & Scaled Interval Data\n", scaled_interval_data) # Create an instance of the OneHotEncoder & Selecting Attributes onehot = preprocessing.OneHotEncoder() hot_array = onehot.fit_transform(imputed_nominal_data).toarray() print(hot_array) from AdvancedAnalytics import linreg from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split

y = df['price'] x = df.drop('price',axis=1) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) lr=LinearRegression() col=[] for i in range(x_train.shape[1]): col.append('X'+str(i)) lr.fit(x_train,y_train) print("\n*** LINEAR REGRESSION ***") linreg.display_coef(lr, x_train, y_train, col) linreg.display_metrics(lr, x_train, y_train) y_hat= lr.predict(x_test) xtestarr = np.asanyarray(x_test) ytestarr = np.asanyarray(y_test) print("Residual sum of squares: %.2f" % np.mean((y_hat - y_test) ** 2)) #Maximum, minimum and mean of predicted value prediction_min = y_hat.min() print("\nPredicted minimum\n",prediction_min) prediction_max = y_hat.max() print("\nPredicted maximum\n",prediction_max) prediction_mean = y_hat.mean() print("\nPredicted mean\n",prediction_mean) #Maximum, minimum and mean of imputed value imputed_min = y_test.idxmin(axis = 0) print("\nImputed minimum\n",imputed_min) imputed_max = y_test.idxmax(axis = 0) print("\nImputed maximum\n",imputed_max) imputed_mean = y_test.mean(axis = 0) print("\nImputed mean\n",imputed_mean) # Printing first 15 predicted values print("\nFirst 15 predicted values\n",y_hat[0:14]) # Printing table final_table = df.head(15) from pandas import ExcelWriter writer = ExcelWriter('PythonHW.xlsx') final_table.to_excel(writer)

writer.save()

2) A listing or screen shot of the min, max and mean of the predicted and actual amounts in your data. Predicted minimum -8484.009878847311 Predicted maximum 27244.01984476174 Predicted mean 3950.5366225183566 Actual minimum 2 Actual maximum 27746 Actual mean 3900.195464095909

3) A listing of the first 15 observations after imputation and prediction. First 15 predicted values [ 131.20374427 7045.54015631 3251.63967449 -152.36320968 6716.05896494 1077.15222802 6289.70190779 -116.45970468 9951.5798897 823.26302334 -242.72890304 3120.9538463 3851.97449455 430.84315957]