Logistic_Regression.py

# Logistic_Regression.py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

# Load dataset
df = pd.read_csv('winequality-white.csv')
X = df.drop('quality', axis=1)
y = df['quality']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic Regression with Grid Search
param_grid = {'C': [0.01, 0.1, 1, 10], 'solver': ['lbfgs', 'liblinear']}
log_reg = GridSearchCV(LogisticRegression(multi_class='multinomial'), param_grid, cv=5)
log_reg.fit(X_train, y_train)

# Make predictions
y_pred = log_reg.predict(X_test)

# Evaluate the model
print("Best Parameters:", log_reg.best_params_)
print(classification_report(y_test, y_pred))
conf_matrix = confusion_matrix(y_test, y_pred)
print(f'Confusion Matrix:\n{conf_matrix}')

# Visualization of confusion matrix
plt.figure(figsize=(8, 6))
plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.show()