Predicting revenue

  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 
 )