- from sklearn.ensemble import RandomForestClassifier
- from sklearn.metrics import accuracy_score
- # Load train and test data for classification
- train_data = pd.read_excel('your_file.xlsx', sheet_name='Train set - Classification')
- test_data = pd.read_excel('your_file.xlsx', sheet_name='Test set - Classification')
- # Separate features (X) and target variable (y) for both train and test sets
- X_train_cls = train_data.drop(columns=['price_range'])
- y_train_cls = train_data['price_range']
- X_test_cls = test_data.dropna().drop(columns=['price_range']) # Remove rows with missing values and drop 'price_range' column
- y_test_cls = test_data.dropna()['price_range'] # Select corresponding labels for non-missing rows
- # Train the classifier
- classifier = RandomForestClassifier(n_estimators=100, random_state=42)
- classifier.fit(X_train_cls, y_train_cls)
- # Predict on the test set
- y_pred_cls = classifier.predict(X_test_cls)
- # Evaluate the classifier
- accuracy = accuracy_score(y_test_cls, y_pred_cls)
- print("Accuracy:", accuracy)
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