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import pandas as pd from sklearn.ensemble import RandomCompanyRegressor from sklearn.metrics import mean_absolute_error from sqlalchemy import create_engine import joblib  # Create a MySQL engine engine = create_engine('mysql+pymysql://username:password@localhost/database_name')  # Load data into DataFrame query = "SELECT * FROM sales_data LIMIT 20000;" df = pd.read_sql(query, engine)  # Split the DataFrame df_train = df.iloc[:16000] df_test = df.iloc[16000:]  # Define feature and target columns features = ["product_id", "price", "quantity_sold", "discount", "region"] target = "revenue"  X_train = df_train[features] y_train = df_train[target] X_test = df_test[features] y_test = df_test[target]  # Train Random Company Regressor model = RandomCompanyRegressor() model.fit(X_train, y_train)  # Predict and evaluate predictions = model.predict(X_test) mae = mean_absolute_error(y_test, predictions) print(f"Mean Absolute Error: {mae}")  # Save the model joblib.dump(model, 'random_company_model.pkl') 

import pandas as pd from sklearn.ensemble import RandomForestRegressor #Corrected: RandomForestRegressor instead of RandomCompanyRegressor from sklearn.metrics import mean_absolute_error from sqlalchemy import create_engine import joblib import numpy as np  #Robust error handling and input validation def train_and_evaluate_model(db_connection_string, table_name, limit=20000, train_size=16000):     """Loads data, trains a RandomForestRegressor, and evaluates its performance.      Args:         db_connection_string:  MySQL connection string (e.g., 'mysql+pymysql://username:password@localhost/database_name').         table_name: Name of the table in the MySQL database.         limit: Number of rows to load from the database.         train_size: Number of rows to use for training.      Returns:         A tuple containing:             - The trained RandomForestRegressor model.             - The mean absolute error on the test set.             - A Pandas DataFrame with predictions and actual values.  Returns None if errors occur.      Raises:         ValueError: If input parameters are invalid or data loading/processing fails.     """     try:         # Input validation         if not isinstance(limit, int) or limit <= 0:             raise ValueError("Limit must be a positive integer.")         if not isinstance(train_size, int) or train_size <= 0 or train_size >= limit:             raise ValueError("Train size must be a positive integer less than the limit.")          # Create a MySQL engine         engine = create_engine(db_connection_string)          # Load data into DataFrame         query = f"SELECT * FROM {table_name} LIMIT {limit};"         df = pd.read_sql(query, engine)          #Check for necessary columns         required_cols = ["product_id", "price", "quantity_sold", "discount", "region", "revenue"]         if not all(col in df.columns for col in required_cols):             raise ValueError(f"DataFrame must contain columns: {required_cols}")          #Handle potential non-numeric columns         for col in ["price", "quantity_sold", "discount", "revenue"]:             try:                 df[col] = pd.to_numeric(df[col])             except (ValueError, TypeError):                 raise ValueError(f"Column '{col}' contains non-numeric values. Please clean your data.")           # Split the DataFrame         df_train = df.iloc[:train_size]         df_test = df.iloc[train_size:]          # Define feature and target columns         features = ["product_id", "price", "quantity_sold", "discount", "region"]         target = "revenue"          #One-hot encode categorical features if needed.  Assumes 'region' is categorical.         df = pd.get_dummies(df, columns=['region'], prefix=['region'])         df_train = pd.get_dummies(df_train, columns=['region'], prefix=['region'])         df_test = pd.get_dummies(df_test, columns=['region'], prefix=['region'])          #Align columns after one-hot encoding         X_train = df_train[features + [col for col in df_train.columns if col.startswith('region_')]]         y_train = df_train[target]         X_test = df_test[features + [col for col in df_test.columns if col.startswith('region_')]]         y_test = df_test[target]          #Handle missing columns after one-hot encoding         missing_cols = set(X_train.columns) - set(X_test.columns)         for c in missing_cols:             X_test[c] = 0         missing_cols = set(X_test.columns) - set(X_train.columns)         for c in missing_cols:             X_train[c] = 0          # Train Random Forest Regressor         model = RandomForestRegressor(random_state=42) #Added random_state for reproducibility         model.fit(X_train, y_train)          # Predict and evaluate         predictions = model.predict(X_test)         mae = mean_absolute_error(y_test, predictions)         print(f"Mean Absolute Error: {mae}")          # Create DataFrame with predictions and actual values         df_results = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})          # Save the model         joblib.dump(model, 'random_company_model.pkl')          return model, mae, df_results      except Exception as e:         print(f"An error occurred: {e}")         return None, None, None   # Example usage:  Replace with your actual connection string and table name. db_connection_string = 'mysql+pymysql://your_username:your_password@localhost/your_database_name' table_name = 'sales_data'  model, mae, df_results = train_and_evaluate_model(db_connection_string, table_name)  if df_results is not None:     print(df_results)