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decision-tree.py
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decision-tree.py
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import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.metrics import confusion_matrix
from sklearn.tree import plot_tree
csv_path = os.getcwd()+'/Data/bank/bank-full.csv'
processed_data = pd.read_csv(csv_path, sep=';')
processed_encoding = processed_data
# prints count of unique values
# print(processed_data["job"].value_counts())
#cleanup_jobs = {"job": {"blue-collar": 1, "management": 2, "technician": 3, "admin.": 4, "services": 5, "retired": 6,
# "self-employed": 7, "entrepreneur": 8, "unemployed": 9, "housemaid": 10, "student": 11,
# "unknown": 12}}
#processed_encoding.replace(cleanup_jobs, inplace=True)
# print(processed_jobs.head())
# print(processed_encoding['marital'].value_counts())
#cleanup_marital = {"marital": {"married": 1, "single": 2, "divorced": 3}}
#processed_encoding.replace(cleanup_marital, inplace=True)
# print(processed_encoding.head())
processed_encoding['job']=pd.factorize(processed_data.job)[0]
processed_encoding['marital']=pd.factorize(processed_data.marital)[0]
# print(processed_encoding["education"].value_counts())
cleanup_education = {"education": {"primary": 1, "secondary": 2, "tertiary": 3, "unknown": 0}}
processed_encoding.replace(cleanup_education, inplace=True)
# print(processed_encoding)
# for default, housing, loan
# print(processed_encoding["loan"].value_counts())
cleanup_boolean = {"default": {"yes": 1, "no": 0},
"housing": {"yes": 1, "no": 0},
"loan": {"yes": 1, "no": 0},
"y": {"yes":1, "no":0}
}
processed_encoding.replace(cleanup_boolean, inplace=True)
# print(processed_encoding.values[1:100, 3:9]
# print(processed_encoding["contact"].value_counts())
#cleanup_contact = {"contact": {"cellular": 1, "telephone": 2, "unknown": 3}}
#processed_encoding.replace(cleanup_contact, inplace=True)
# print(processed_encoding[['age']])
# print(processed_encoding["month"].value_counts())
#cleanup_month = {"month": {"jan": 1, "feb":2, "mar":3, "apr":4, "may":5, "jun": 6, "jul":7, "aug":8, "sep":9,
# "oct":10, "nov":11, "dec":12}}
#processed_encoding.replace(cleanup_month, inplace=True)
# print(processed_encoding[['month']])
processed_data['contact']=pd.factorize(processed_data.contact)[0]
processed_data['month']=pd.factorize(processed_data.month)[0]
# print(processed_encoding["poutcome"].value_counts())
cleanup_poutcome = {"poutcome": {"success":1, "failure":2, "other":3, "unknown":4}}
processed_encoding.replace(cleanup_poutcome, inplace=True)
# print(processed_encoding[['poutcome']])
# print("data length :: ", len(processed_data))
# print("no null data :: ", processed_data.isnull().sum())
# print(processed_data.head())
#processed data frame to csv file
processed_encoding.to_csv('processed_data.csv')
# processed_data.info()
X = processed_encoding.iloc[1:, 0:15]
Y = processed_encoding.iloc[1:, -1]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
def decision_tree():
cal_entropy = DecisionTreeClassifier(criterion="entropy", random_state=1, max_depth=3, min_samples_leaf=5)
cal_entropy.fit(X_train, Y_train)
Y_pred = cal_entropy.predict(X_test)
print('Decision tree accuracy: ', metrics.accuracy_score(Y_test, Y_pred))
#tree.plot_tree(cal_entropy.fit(processed_encoding.X_train, processed_encoding.Y_train))
print("Confusion matrix for decision tree:")
print(confusion_matrix(Y_test,Y_pred))
tree.plot_tree(cal_entropy)
plt.show()
# PRoc data: 0.8897736488977365
def random_forest():
# Create the model with 100 trees
random_forest_model = RandomForestClassifier(n_estimators=100, bootstrap=True, max_features='sqrt')
# Fit on training data
random_forest_model.fit(X_train, Y_train)
Y_pred = random_forest_model.predict(X_test)
print("Random forest accuracy: ",metrics.accuracy_score(Y_test,Y_pred))
print("Confusion matrix for random forest:")
print(confusion_matrix(Y_test,Y_pred))
# 0.9023077490230775
def naive_bayes():
model = GaussianNB()
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)
print("Naive bayes accuracy: ", metrics.accuracy_score(Y_test, Y_pred))
print("Confusion matrix for naive bayes:")
print(confusion_matrix(Y_test,Y_pred))
# 0.8607977586079776
def kmeans():
# Elbow method for number of clusters
Error = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i).fit(X_test)
kmeans.fit(X_test)
Error.append(kmeans.inertia_)
plt.plot(range(1, 11), Error)
plt.title('Elbow method')
plt.xlabel('No of clusters')
plt.ylabel('Error')
plt.show()
print("-----------------------------")
# kmeans implementation with 3 clusters
kmeans = KMeans(n_clusters=3, init='k-means++', random_state=42)
y_kmeans = kmeans.fit_predict(X_test)
# print(y_kmeans3)
# print(kmeans3.cluster_centers_)
# plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=100, c='red', label='Cluster 1')
# plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=100, c='blue', label='Cluster 2')
# plt.scatter(X.iloc[:, 0], X.iloc[:, 1], s=100, c='green', label='Cluster 3')
# visualising kmeans
plt.scatter(X_test.iloc[:,0], X_test.iloc[:,1],c=y_kmeans,cmap='rainbow')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='yellow', label='Centroids')
plt.title("Clusters")
plt.xlabel("X-label")
plt.ylabel("y-label")
plt.legend()
plt.show()
random_forest()
naive_bayes()
decision_tree()
kmeans()