import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

campaign = pd.read_csv('https://raw.githubusercontent.com/byui-cse/cse450-course/master/data/bank.csv')
campaign

	age	job	marital	education	default	housing	loan	contact	month	day_of_week	campaign	pdays	previous	poutcome	emp.var.rate	cons.price.idx	cons.conf.idx	euribor3m	nr.employed	y
0	56	housemaid	married	basic.4y	no	no	no	telephone	may	mon	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
1	57	services	married	high.school	unknown	no	no	telephone	may	mon	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
2	37	services	married	high.school	no	yes	no	telephone	may	mon	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
3	40	admin.	married	basic.6y	no	no	no	telephone	may	mon	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
4	56	services	married	high.school	no	no	yes	telephone	may	mon	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37064	73	retired	married	professional.course	no	yes	no	cellular	nov	fri	1	999	0	nonexistent	-1.1	94.767	-50.8	1.028	4963.6	yes
37065	46	blue-collar	married	professional.course	no	no	no	cellular	nov	fri	1	999	0	nonexistent	-1.1	94.767	-50.8	1.028	4963.6	no
37066	56	retired	married	university.degree	no	yes	no	cellular	nov	fri	2	999	0	nonexistent	-1.1	94.767	-50.8	1.028	4963.6	no
37067	44	technician	married	professional.course	no	no	no	cellular	nov	fri	1	999	0	nonexistent	-1.1	94.767	-50.8	1.028	4963.6	yes
37068	74	retired	married	professional.course	no	yes	no	cellular	nov	fri	3	999	1	failure	-1.1	94.767	-50.8	1.028	4963.6	no

37069 rows × 20 columns

filtered_df = campaign[~campaign.apply(lambda row: row.astype(str).str.contains('unknown|999').any(), axis=1)]
filtered_df = filtered_df.dropna()
filtered_df

	age	job	marital	education	default	housing	loan	contact	month	day_of_week	campaign	pdays	previous	poutcome	emp.var.rate	cons.price.idx	cons.conf.idx	euribor3m	nr.employed	y
21712	37	admin.	married	university.degree	no	yes	no	telephone	nov	wed	1	6	1	success	-0.1	93.200	-42.0	4.286	5195.8	no
21851	48	management	married	university.degree	no	yes	no	cellular	nov	mon	1	4	1	success	-0.1	93.200	-42.0	4.191	5195.8	no
22047	36	self-employed	single	university.degree	no	yes	no	cellular	nov	mon	2	4	1	success	-0.1	93.200	-42.0	4.191	5195.8	no
22163	32	blue-collar	married	basic.9y	no	no	no	cellular	nov	mon	2	5	1	success	-0.1	93.200	-42.0	4.191	5195.8	yes
22328	31	entrepreneur	divorced	basic.4y	no	no	no	cellular	nov	tue	1	5	1	success	-0.1	93.200	-42.0	4.153	5195.8	no
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
37045	60	blue-collar	married	basic.4y	no	yes	no	cellular	nov	tue	2	4	1	success	-1.1	94.767	-50.8	1.035	4963.6	no
37046	35	technician	divorced	basic.4y	no	yes	no	cellular	nov	tue	3	4	2	success	-1.1	94.767	-50.8	1.035	4963.6	yes
37047	54	admin.	married	professional.course	no	no	no	cellular	nov	tue	2	10	1	success	-1.1	94.767	-50.8	1.035	4963.6	yes
37057	62	retired	married	university.degree	no	yes	no	cellular	nov	thu	1	1	6	success	-1.1	94.767	-50.8	1.031	4963.6	yes
37060	62	retired	married	university.degree	no	no	no	cellular	nov	thu	2	6	3	success	-1.1	94.767	-50.8	1.031	4963.6	yes

1193 rows × 20 columns

filtered_df.columns

Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',
       'contact', 'month', 'day_of_week', 'campaign', 'pdays', 'previous',
       'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx',
       'euribor3m', 'nr.employed', 'y'],
      dtype='object')

X = pd.get_dummies(filtered_df[['job', 'marital', 'education', 'default', 'housing', 'loan', 'age', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx',
       'euribor3m', 'nr.employed']], drop_first=True)
y = pd.get_dummies(filtered_df['y'], drop_first=True)

# Split our data into training and test data, with 30% reserved for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

# Build the decision tree
clf = DecisionTreeClassifier()

# Train it
clf.fit(X_train, y_train)

# Test it
clf.score(X_test, y_test)

0.6033519553072626

# Generate the confusion matrix
cm = confusion_matrix(y_test, clf.predict(X_test))

# Display the confusion matrix
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)
disp.plot()
plt.show()

fig, ax = plt.subplots(figsize=(20, 20))
tree.plot_tree(clf, fontsize=10, feature_names=X.columns)
plt.show()