feat: initial solution to the task

lab4/main.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, mean_squared_error
+
+filename = '/home/kuchy/EARIN/lab4/variant2.csv'
+
+# Load the dataset
+wine_data = pd.read_csv(filename)
+
+# Split into features and labels
+X = wine_data.drop("quality", axis=1)
+y = wine_data["quality"]
+
+# Split 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)
+
+# Linear Regression
+lin_reg = LinearRegression()
+lin_reg.fit(X_train, y_train)
+y_pred_lin = lin_reg.predict(X_test)
+lin_reg_rmse = mean_squared_error(y_test, y_pred_lin, squared=False)
+
+# Logistic Regression
+log_reg = LogisticRegression(multi_class='multinomial', solver='newton-cg')
+log_reg.fit(X_train, y_train)
+y_pred_log = log_reg.predict(X_test)
+log_reg_accuracy = accuracy_score(y_test, y_pred_log)
+
+# SVM
+svm = SVC()
+svm.fit(X_train, y_train)
+y_pred_svm = svm.predict(X_test)
+svm_accuracy = accuracy_score(y_test, y_pred_svm)
+
+# Compare performance
+print("Linear Regression RMSE:", lin_reg_rmse)
+print("Logistic Regression accuracy:", log_reg_accuracy)
+print("SVM accuracy:", svm_accuracy)