Logistic Regression(로지스틱 회귀) 가장 많이 사용하는 파이썬 알고리즘 part2.

Logistic Regression입니다. 기본 예제입니다. 이전 포스트인 Linear Regression과 동일한 데이터(타이타닉 생존자)를 사용하였습니다.

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load data into a Pandas DataFrame
data = pd.read_csv("data.csv")

# Split the data into features (X) and target variable (y)
X = data[["feature1", "feature2", ...]]
y = data["target"]

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train the logistic regression model on the training data
clf = LogisticRegression().fit(X_train, y_train)

# Make predictions on the test data
y_pred = clf.predict(X_test)

# Evaluate the model's performance
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

data.csv

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한번 코드 돌려보시면서, 모델 정확도를 측정해보세요