Code

#importing the Libraies
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset=pd.read_csv("Social_Network_Ads.csv")

dataset

dataset=pd.get_dummies(dataset,drop_first=True)

dataset

dataset=dataset.drop("User ID",axis=1)

dataset["Purchased"].value_counts()

indep=dataset[["Age","EstimatedSalary","Gender_Male"]]
dep=dataset["Purchased"]

indep.shape

dep

#split into training set and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(indep, dep, test_size = 1/3, random_state = 0)

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
        

y_pred = classifier.predict(X_test)
        

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
        

from sklearn.metrics import classification_report
clf_report = classification_report(y_test, y_pred)

print(clf_report)

print(cm)

classifier.predict([[40,300,1,]])

Dataset

User Purchase Data

User ID	Gender	Age	Estimated Salary	Purchased
15624510	Male	19	19000	0
15810944	Male	35	20000	0
15668575	Female	26	43000	0
15603246	Female	27	57000	0
15804002	Male	19	76000	0
15728773	Male	27	58000	0
15598044	Female	27	84000	0
15694829	Female	32	150000	1
15600575	Male	25	33000	0
15727311	Female	35	65000	0
15570769	Female	26	80000	0
15606274	Female	26	52000	0
15746139	Male	20	86000	0
15704987	Male	32	18000	0
15628972	Male	18	82000	0
15697686	Male	29	80000	0
15733883	Male	47	25000	1
15617482	Male	45	26000	1
15704583	Male	46	28000	1
15621083	Female	48	29000	1
15649487	Male	45	22000	1
15736760	Female	47	49000	1
15714658	Male	48	41000	1
15599081	Female	45	22000	1
15705113	Male	46	23000	1
15631159	Male	47	20000	1
15792818	Male	49	28000	1
15633531	Female	47	30000	1
15744529	Male	29	43000	0
15669656	Male	31	18000	0
15581198	Male	31	74000	0
15729054	Female	27	137000	1
15573452	Female	21	16000	0
15776733	Female	28	44000	0
15724858	Male	27	90000	0
15713144	Male	35	27000	0
15690188	Female	33	28000	0
15689425	Male	30	49000	0
15671766	Female	26	72000	0
15782806	Female	27	31000	0
15764419	Female	27	17000	0
15591915	Female	33	51000	0
15772798	Male	35	108000	0
15792008	Male	30	15000	0
15715541	Female	28	84000	0
15639277	Male	23	20000	0
15798850	Male	25	79000	0
15776348	Female	27	54000	0
15727696	Male	30	135000	1
15793813	Female	31	89000	0
15694395	Female	24	32000	0
15764195	Female	18	44000	0
15744919	Female	29	83000	0
15671655	Female	35	23000	0
15654901	Female	27	58000	0
15649136	Female	24	55000	0
15775562	Female	23	48000	0
15807481	Male	28	79000	0
15642885	Male	22	18000	0
15789109	Female	32	117000	0
15814004	Male	27	20000	0
15673619	Male	25	87000	0
15595135	Female	23	66000	0
15583681	Male	32	120000	1
15605000	Female	59	83000	0
15718071	Male	24	58000	0
15679760	Male	24	19000	0
15654574	Female	23	82000	0
15577178	Female	22	63000	0
15595324	Female	31	68000	0
15756932	Male	25	80000	0
15726358	Female	24	27000	0
15595228	Female	20	23000	0
15782530	Female	33	113000	0
15592877	Male	32	18000	0
15651983	Male	34	112000	1
15746737	Male	18	52000	0
15774179	Female	22	27000	0
15667265	Female	28	87000	0
15655123	Female	26	17000	0
15595917	Male	30	80000	0
15668385	Male	39	42000	0
15709476	Male	20	49000	0
15711218	Male	35	88000	0
15798659	Female	30	62000	0
15663939	Female	31	118000	1
15694946	Male	24	55000	0
15631912	Female	28	85000	0
15768816	Male	26	81000	0
15682268	Male	35	50000	0
15684801	Male	22	81000	0
15636428	Female	30	116000	0
15809823	Male	26	15000	0
15699284	Female	29	28000	0
15786993	Female	29	83000	0
15709441	Female	35	44000	0
15710257	Female	35	25000	0
15582492	Male	28	123000	1
15575694	Male	35	73000	0