Random Forest Classification Accuracy Calculator

This function uses the Random Forest algorithm to classify the given data and returns the accuracy of the model. First, the data is split into training and testing sets, then a Random Forest classifier is initialized, trained, and predictions are made on the test set, finally the accuracy is calculated.

Technology Stack : scikit-learn

Code Type : Machine learning classification

Code Difficulty : Intermediate

                
                    
def random_forest_classification(X, y):
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    
    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    
    # Initialize the Random Forest Classifier
    clf = RandomForestClassifier(n_estimators=100, random_state=42)
    
    # Train the model
    clf.fit(X_train, y_train)
    
    # Predict the labels for the test set
    y_pred = clf.predict(X_test)
    
    # Calculate the accuracy of the model
    accuracy = accuracy_score(y_test, y_pred)
    
    return accuracy