"""
DataDrivenPrediction
Generated by Eden via recursive self-improvement
2025-11-01 06:44:28.154667
"""

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# Function to load and preprocess data
def load_data(file_path):
    """
    Load and preprocess data from a CSV file.
    
    Parameters:
        - file_path (str): Path to the CSV file containing the dataset.
        
    Returns:
        - X (DataFrame): Features of the dataset.
        - y (Series): Target variable of the dataset.
    """
    df = pd.read_csv(file_path)
    # Preprocessing steps can be added here
    return df.drop('target_column', axis=1), df['target_column']

# Function to train and evaluate a model
def train_model(X, y):
    """
    Train a machine learning model and evaluate its performance.
    
    Parameters:
        - X (DataFrame): Features of the dataset.
        - y (Series): Target variable of the dataset.
        
    Returns:
        - model: Trained machine learning model.
        - predictions: Predictions made by the trained model.
    """
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Train a RandomForestRegressor
    model = RandomForestRegressor(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    
    # Make predictions and evaluate the model
    predictions = model.predict(X_test)
    rmse = mean_squared_error(y_test, predictions, squared=False)
    return model, predictions, rmse

# Example usage
if __name__ == "__main__":
    file_path = "path/to/your/dataset.csv"
    X, y = load_data(file_path)
    model, predictions, rmse = train_model(X, y)
    print(f"Model RMSE: {rmse}")