Training CatBoost Model on Boston Housing Dataset for Predictions

This function trains a CatBoost regression model on the Boston Housing dataset and returns the predictions on the test set.

Technology Stack : CatBoost, scikit-learn

Code Type : Function

Code Difficulty : Intermediate

                
                    
def random_predict(input_features):
    import catboost as cb
    from sklearn.datasets import load_boston
    from sklearn.model_selection import train_test_split

    # Load Boston Housing dataset
    boston = load_boston()
    X = boston.data
    y = boston.target

    # Split the dataset into train and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Create a CatBoost model
    model = cb.CatBoostRegressor()

    # Train the model
    model.fit(X_train, y_train)

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

    return predictions