Explain how to implement decision tree in python

Answered by Ira Joshi

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))

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