Predict User Recommendations with CatBoost Classifier

This function creates a CatBoost classifier and trains it using a randomly generated dataset. It then predicts recommendations for a given user ID.

Technology Stack : CatBoost, NumPy, Pandas

Code Type : Function

Code Difficulty : Intermediate

                
                    
import catboost as cb
import numpy as np
import pandas as pd

def predict_user_recommendations(user_id, user_data):
    # Create a CatBoost model
    model = cb.CatBoostClassifier()

    # Generate a random dataset
    np.random.seed(0)
    n_users, n_features = 1000, 50
    user_ids = np.arange(n_users)
    features = np.random.rand(n_users, n_features)
    labels = np.random.randint(0, 2, n_users)

    # Create a DataFrame from the generated data
    df = pd.DataFrame(features, columns=[f'feature_{i}' for i in range(n_features)])
    df['user_id'] = user_ids
    df['label'] = labels

    # Train the model
    model.fit(df.drop(['user_id', 'label'], axis=1), df['label'])

    # Predict the recommendations for the given user
    user_features = user_data.drop(['user_id'], axis=1)
    user_id_index = user_data['user_id'].iloc[0]
    predictions = model.predict(user_features)

    return user_id_index, predictions