salesanalyzer_mds.predict_sales
===============================

.. py:module:: salesanalyzer_mds.predict_sales


Functions
---------

.. autoapisummary::

   salesanalyzer_mds.predict_sales.predict_sales


Module Contents
---------------

.. py:function:: predict_sales(sales_data, new_data, numeric_features, categorical_features, target, date_feature=None, test_size=0.3)

   Predicts future sales based on the provided historical data.

   Parameters:
   -----------
   sales_data: pd.DataFrame
       DataFrame containing historical sales data.
   new_data: pd.DataFrame
       DataFrame containing new data to predict on.
   numeric_features: list
       List of columns to use as features with numeric data type.
   categorical_features: list
       List of columns to use as features with character data type.
   target: str
       Name of the target column.
   date_feature: str
       Name of columns to use as features with datetime data type.
       Default: None
   test_size: float
       Proportion of data to be used for testing.
       Default value is 0.3

   Returns:
   --------
   pd.DataFrame:
       A data frame with prediction values, and a printed out MSE score and R^2 score.

   Examples:
   ---------
   >>> sales_data = pd.DataFrame({'name': ['laptop', 'monitor'], 'price': [100, 200], 'quantity': [2, 1]})
   >>> new_data = pd.DataFrame({'name': 'laptop', 'price' : 300})
   >>> numeric_features = ['price']
   >>> categorical_features = ['name']
   >>> target = 'quantity'
   >>> predict_sales(sales_data, new_data, numeric_features, categorical_features, target)
       MSE of the model: 1.02,
           Predicted values
       0   245.40