salesanalyzer_mds.predict_sales

Functions

predict_sales(sales_data, new_data, numeric_features, ...)

Predicts future sales based on the provided historical data.

Module Contents

salesanalyzer_mds.predict_sales.predict_sales(sales_data, new_data, numeric_features, categorical_features, target, date_feature=None, test_size=0.3)[source]

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