Question 3:For the following politically incorrect data from Quinlan:Height HairSmallTallTallTallSmallTallTallSmallBlondeDarkBlondeDarkDarkRedBlondeBlondeEyesBrownBrownBlueBlueBlueBlueBrownBlueAttractive?NoNoYesNoNoYesNoYes1. Turn this data into a decision tree to classify which attributes make a person attractive, and thenextract the rules. No programming use paper and pencil.2.(Using Scikit-Learn train a decision tree to predict whether a person with a small height, darkhair, and brown eyes is an attractive person or not

Height Hair Eyes Attractive small Blonde Brown No Tall Dark Brown No Tall Blonde Blue Yes Tall Dark Blue No small Dark Blue No Tall Red Blue Yes Tall Blonde Brown No small Blonde Blue Yes1) Using the probability the decision tree is generated Height Probability Small 3 3/8 Tall 5 5/8 Height Hair Probability Small Blonde 2/8 Small Dark 1/8 Small Red 0/8 Tall Blonde 2/8 Tall Dark 2/8 Tall Red 1/8 Height Hair Eyes Probability Small Blonde Brown 1/8 Small Blonde Blue 1/8 Small Dark Brown 0/8 Small Dark Blue 1/8 Small Red Brown 0/8 Small Red Blue 0/8 Tall Blonde Brown 1/8 Tall Blonde Blue 1/8 Tall Dark Brown 1/8 Tall Dark Blue 1/8 Tall Red Brown 0/8 Tall Red Blue 1/8 Height Hair Eyes Attraction Probability Small Blonde Brown No 1/8 Small Blonde Blue Yes 1/8 Small Dark Brown -NA- 0/8 Small Dark Blue No 1/8 Small Red Brown -NA- 0/8 Small Red Blue -NA- 0/8 Tall Blonde Brown No 1/8 Tall Blonde Blue Yes 1/8 Tall Dark Brown No 1/8 Tall Dark Blue No 1/8 Tall Red Brown -NA- 0/8 Tall Red Blue Yes 1/8 Hence the decision Tree will be as shown below:import pandas as pd data = pd.read_csv('/content/attract.csv') # show the first 5 lines of the dataframe data.head() data_x = data.drop('Attractive', axis=1) data_y=data['Attractive'] data_x_encoded = pd.get_dummies(data_x, drop_first=True) data_x_encoded from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(data_x_encoded, data_y, test_size=0.3) 2) from sklearn.tree import DecisionTreeClassifier dtree = DecisionTreeClassifier(max_depth=5) dtree.fit(X_train, y_train) from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score dtree = DecisionTreeClassifier(max_depth=2) dtree.fit(X_train, y_train) predicitions = dtree.predict(X_test) acc = accuracy_score(y_test, predicitions) X_test print(predicitions) ['No' 'No' 'No'] testdata = [[1,1,0,1]] df = pd.DataFrame(testdata,columns=['Height_small','Hair_Dark','Hair_Red','Eyes_Brown']) predicitions = dtree.predict(df) print(predicitions) ['No'] Hence, the output if the person is small, with dark hair, brown eyes will not be attractive