python을 이용한 Wine Quality dataset Logistic Regression

Python을 이용한 UCI Wine Quality 데이터 Logistic Regression

데이터셋 다운로드

https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/

UCI Wine Quality Data set

Attribute information

• 1 - fixed acidity
• 2 - volatile acidity
• 3 - citric acid
• 4 - residual sugar
• 5 - chlorides
• 6 - free sulfur dioxide
• 7 - total sulfur dioxide
• 8 - density
• 9 - pH
• 10 - sulphates
• 11 - alcohol
• 12 - quality (score between 0 and 10)

코드 구현 

CSV 데이터 확인.

import pandas as pd

import statsmodels.api as sm

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

wine_data = pd.read_csv('winequality-white.csv',delimiter=';',dtype=float)

wine_data.head(10)

데이터 자르기 및 qulity 변수 값 변경.

x_data = wine_data.iloc[:,0:-1]

y_data = wine_data.iloc[:,-1]

# Score 값이 7보다 작으면 0,  7보다 크거나 같으면 1로 값 변경.

y_data = np.array([1 if i>=7 else 0 for i in y_data])

x_data.head(5)

# 트레인, 테스트 데이터 나누기.

train_x, test_x, train_y, test_y = sklearn.model_selection.train_test_split(x_data, y_data, test_size = 0.3,random_state=42)

로지스틱 리그레션 모델 학습

log_reg = LogisticRegression()

log_reg.fit(train_x, train_y)

성능 평가

y_pred = log_reg.predict(test_x)

print("Train Data:",log_reg.score(train_x,train_y))

print("Test Data",sum(y_pred == test_y) / len(test_y))

from sklearn.metrics import classification_report

y_true, y_pred = test_y, log_reg.predict(test_x)

print(classification_report(y_true, y_pred))

odds ratio

print (np.exp(logit.params))

params = logit.params

conf = logit.conf_int()

conf['OR'] = params

conf.columns = ['2.5%', '97.5%', 'OR']

print (np.exp(conf))

MLE

logit = sm.Logit(train_y,train_x).fit()

logit.summary()