updated README file

README.md

@@ -2,6 +2,22 @@
 
 A package providing functions to calculate key regression metrics: R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE).
 
+## Features
+
+This package consists of four functions:
+- `r_squared`:
+    - This function calculates the R-squared of the model, which measures how well the model explains the variation in the data. 
+- `mean_absolute_error`: 
+    - This function finds the average difference between predicted and actual values.
+- `mean_squared_error`:
+    - This function calculates the average of the squared differences between predictions and actual values. 
+- `mean_absolute_percentage_error`:
+    - This function shows prediction error as a percentage, making it easy to understand.
+
+##  matrics_calculator in the Python Ecosystem
+
+`matrics_calculator` works alongside Python libraries like `scikit-learn` by providing simple implementations of regression metrics. Unlike `scikit-learn`’s full toolkit for modeling and evaluation, this package focuses only on metrics, making it easy to use for quick analysis or custom workflows.
+
 ## Installation
 
 ```bash
@@ -23,3 +39,10 @@ Interested in contributing? Check out the contributing guidelines. Please note t
 ## Credits
 
 `matrics_calculator` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
+
+## Constributors
+
+- Celine Habashy
+- Jay Mangat
+- Yajing Liu
+- Zhiwei Zhang