Updated readme to include price_threshold parameter

README.md

@@ -14,6 +14,7 @@ $ pip install salesanalyzer_mds
 ```
 
 ## Functions
+
 - `segment_revenue_share`: Segments products into three categories: cheap, medium, expensive, based on price, and calculates their respective share in total revenue. 
 - `predictSales`: Predicts future sales based on the provided historical data and the target.
 - `sales_summary_statistics`: Calculates a variety of summary statistics that provide insights into overall sales performance,
@@ -36,10 +37,13 @@ import pandas as pd     # additional import to handle your sales data
 3. Retrieve the insights:
 
 **Summary statistics**
+
 ```
 sales_summary_statistics(your_sales_data)
 ```
-The `sales_summary_statistics` returns a pandas DataFrame with:
+
+The `sales_summary_statistics()` function returns a pandas DataFrame with:
+
 - 'total_revenue': The total revenue generated by all sales.
 - 'unique_customers': The number of unique customers.
 - 'average_order_value': The average value of an order (sum of revenue per invoice).
@@ -48,15 +52,22 @@ The `sales_summary_statistics` returns a pandas DataFrame with:
 - 'average_revenue_per_customer': The average revenue generated by each customer.
 
 **Segment revenue share**
+
 ```
 segment_revenue_share(your_sales_data, 
                       price_col='UnitPrice', 
-                      quantity_col='Quantity')      # replace column names with your data column names
+                      quantity_col='Quantity',
+                      price_thresholds=None)      # replace column names with your data column names
 ```
-The `segment_revenue_share` returns a pandas DataFrame showing the total revenue share for each price segment:
-'cheap', 'medium', 'expensive'.
+
+The `segment_revenue_share()` funtion returns a pandas DataFrame showing the total revenue share for each price segment:
+'cheap', 'medium', 'expensive'. Custom price thresholds can be set by the user other set automatically.
+
+- Custom price thresholds can be set using the `price_thresholds` parameter.
+- If not specified, thresholds are automatically determined based on the data.
 
 **Predict sales**
+
 ```
 predict_sales(your_sales_data, 
               new_data,     # new sales data to base the predictions on
@@ -65,12 +76,14 @@ predict_sales(your_sales_data,
               target = 'Quantity', 
               date_feature = 'InvoiceDate')
 ```
-The `predict_sales` returns a DataFrame with prediction values, and a printed out MSE score.
+
+The `predict_sales()` function returns a DataFrame with prediction values, and a printed out MSE score.
 
 ## Developer notes:
 ### Running The Tests
 
 Run the following command in the terminal from the project's root directory to execute the tests:
+
 ```bash
 pytest tests/
 ```