## Python - Classification - Decission Tree (DT)

Data:

Questionnaire data from mall visitors contains sex, age, salary & shopping score (200 rows)

Mission:

How to predict the probability of shopping score from given age & salary

Library used:

• Pandas
• Matplotlib
• Scikit

Code:

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeRegressor

url = 'https://raw.githubusercontent.com/kokocamp/vlog101/master/vlog101.csv'
vlog123.describe()

X = vlog123[['Usia','Gaji (juta)']]
y = vlog123['Skor Belanja (1-100)']

d3 = DecisionTreeRegressor()
d3.fit(X, y)

usia = input("Usia (thn): ")
usia = int(usia)
gaji = input("Gaji (juta): ")
gaji = int(gaji)
data = [usia,gaji]
print("Prediksi Skor Belanja (1-100): ", d3.predict([data]))

plt.scatter(vlog123[['Gaji (juta)']],y, color='green')
plt.scatter(gaji,d3.predict([data]), color='red')

I wrapped the scenario in a Youtube video below.

Click this link (http://paparadit.blogspot.com/2020/11/the-algorithms-of-machine-learning.html), if you want to check out for other algorithms. Thank you for for visiting this blog & subs my channel.

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