randomForestIncome.py

def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier

income_data = pd.read_csv("income.csv", header = 0, delimiter = ", ")

labels = income_data[["income"]]

income_data["sex-int"] = income_data["sex"].apply(lambda row: 0 if row == "Male" else 1)
income_data["country-int"] = income_data["native-country"].apply(lambda row: 0 if row == "United States" else 1)


data = income_data[["age", "capital-gain", "capital-loss", "hours-per-week", "sex-int", "country-int"]]

train_data, test_data, train_labels, test_labels = train_test_split(data, labels, random_state = 1)

forest = RandomForestClassifier(random_state = 1)

forest.fit(train_data, train_labels)

print(forest.score(test_data, test_labels))