Explain how to implement decision tree in python

Initially we import the libraries and the dataset

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv("Fraud_check.csv")

data.head()

Now we discretize the data

data.TaxableIncome.max()

data['TaxableIncome'] = pd.cut(data['TaxableIncome'], [0,30000,100000], labels=['Risky','Good'])

data_dummies=pd.get_dummies(data[['Undergrad','Marital.Status','Urban']])

data_new=data.drop(['Undergrad','Marital.Status','Urban'],place=True,axis=1)

data_new=pd.concat([data,data_dummies],axis=1)

Now we split the dataset for training and testing

X=pd.DataFrame(data_new.iloc[:,1:].values)

y=data_new['TaxableIncome']

data_new.isnull().sum()

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)

Now we will fit the model and predict the data

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()

model.fit(X,y)

predictions=model.predict(X_test)

predictions=pd.DataFrame(predictions)

Now we will evaluate the model

from sklearn.metrics import classification_report,confusion_matrix

print(confusion_matrix(y_test,predictions))

print('
')

print(classification_report(y_test,predictions))