Pima Indians Diabetes – Logistic Regression Practice

This notebook is designed to practice Logistic Regression using the Pima Indians Diabetes Dataset.

📊 Dataset Overview

• Source: UCI Machine Learning Repository

• 출처: UCI Machine Learning Repository

• Problem: A binary classification problem to predict diabetes based on health information of Pima Indian women.

• 문제: 피마 인디언 여성의 건강 정보를 바탕으로 당뇨병 여부를 예측하는 이진 분류 문제.

• Number of Features: 8

• 특성 수: 8

• Target Variable: Diabetes status (1 = Diabetes, 0 = No Diabetes)

• 목표 변수: 당뇨병 유무 (1 = 있음, 0 = 없음)

📥 Data Download

The data can be downloaded from the link below:

🔗 https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database

Alternatively, you can use a locally saved CSV file named 07_2_diabetes.csv.
또는 07_2_diabetes.csv라는 이름으로 로컬에 저장된 CSV 파일 사용 가능.

📂 Load Data

import pandas as pd

df = pd.read_csv('07_2_diabetes.csv')
df.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

🧪 Split train/test dataset

from sklearn.model_selection import train_test_split
X = df.drop('Outcome', axis=1)
y = df['Outcome']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

🧠 Learn Logistic Model

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

LogisticRegression(max_iter=1000)

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📈 Prediction & Evaluation

from sklearn.metrics import accuracy_score
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')

Accuracy: 0.75

📊 Visualization (matplotlib)

import matplotlib.pyplot as plt
import numpy as np

labels = ['No Diabetes', 'Diabetes']
actual_counts = np.bincount(y_test)
predicted_counts = np.bincount(y_pred)

x = np.arange(len(labels))
width = 0.35

fig, ax = plt.subplots()
ax.bar(x - width/2, actual_counts, width, label='Actual')
ax.bar(x + width/2, predicted_counts, width, label='Predicted')

ax.set_xlabel('Outcome')
ax.set_ylabel('Count')
ax.set_title('Actual vs Predicted Counts')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()

plt.tight_layout()
plt.show()