I get 100% on my test set using random forest. What is wrong?

Question

I am getting 100% accuracy on my test set when trained using random forest.

Is there something wrong with my model?

Code:

import pandas as pd 
import numpy as np 
from sklearn.model_selection import train_test_split 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import accuracy_score 
from sklearn import preprocessing 
from sklearn.metrics import classification_report, confusion_matrix 
from sklearn.preprocessing import OneHotEncoder 
from sklearn.compose import ColumnTransformer 
from sklearn.pipeline import Pipeline 
from sklearn.preprocessing import StandardScaler

ds = pd.read_csv('census-income.test(no unk.).csv')

df = pd.read_csv('census-income.data(no unk.).csv')

X = df 
y = df['income']

X_T = ds 
y_T = ds['income']

categorical_preprocessor = Pipeline(steps=[ ("onehot", OneHotEncoder(handle_unknown="ignore")) ])

preprocessor = ColumnTransformer([ ("categorical", categorical_preprocessor, ['workclass','education','martial-status','occupation','relationship','race','sex', 'native-country','income']), ],remainder='passthrough')

pipe = Pipeline(steps=[ ("preprocessor", preprocessor), ("classifier", RandomForestClassifier(n_estimators=128, max_depth=7)) ])

X_train = X 
X_test = X_T 
y_train = y 
y_test = y_T

pipe.fit(X_train, y_train) 
y_pred = pipe.predict(X_test)

print(classification_report(y_test, y_pred, digits=4)) 
print(confusion_matrix(y_test, y_pred)) 

Confusion matrix

[[11360     0]
 [    0  3700]]

Training data

Test data

Answer 1

I think you leak the answer when u defining this variable.

X_T = ds 
y_T = ds['income']

So when u predicting the test set the data perfectly predict 100% accuracy

y_pred = pipe.predict(X_test)

You can try it like this for train and test data:

X_T = ds.drop(['income'],axis=1)
y_T = ds['income']