wine_quality_model.py

# Import necessary libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV

# Read the wine quality dataset
data = pd.read_csv('/content/winequality-red.csv')

# Handle missing values (if any)
data.dropna(inplace=True)

# Create feature matrix (X) and target vector (y)
X = data.drop('quality', axis=1)
y = data['quality']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the parameter grid for hyperparameter tuning
param_grid = {'n_estimators': [100, 200, 300],
              'max_depth': [5, 10, 15],
              'min_samples_leaf': [1, 2, 3]}

# Create a Random Forest Classifier object
rf_model = RandomForestClassifier()

# Perform grid search cross-validation to find optimal hyperparameters
grid_search = GridSearchCV(rf_model, param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train, y_train)

# Get the best hyperparameters from the grid search
best_params = grid_search.best_params_
print("Best Hyperparameters:", best_params)

# Train the Random Forest Classifier with the best hyperparameters
rf_model.set_params(**best_params)
rf_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = rf_model.predict(X_test)

# Calculate the accuracy score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# Plot the prediction graph
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred)
plt.xlabel('Actual Quality')
plt.ylabel('Predicted Quality')
plt.title('Prediction Graph')
plt.show()