Update README.md

Author: yildiramdsa

README.md

@@ -4,4 +4,4 @@ This project presents data-driven solutions to optimize departmental operations
 
 ![Data-Driven Department Optimization](https://github.com/yildiramdsa/data_driven_department_optimization/blob/main/images/data_driven_department_optimization.png)
 
-In the **Human Resources Department**, models like **Logistic Regression**, **Random Forest**, and **Deep Learning** predict employee turnover, offering insights into retention strategies. For the **Marketing Department**, techniques like **K-Means**, **PCA**, and **Autoencoders** enable effective customer segmentation for targeted campaigns. The **Sales Department** benefits from time series forecasting using **Facebook Prophet** to predict daily sales, ensuring efficient inventory management. In the **Operations Department**, **deep learning models** are employed for chest disease detection, supporting medical diagnostics. Finally, the **Public Relations Department** utilizes **Naive Bayes** and **Logistic Regression** to predict customer satisfaction and improve engagement strategies.
+In the Human Resources Department, **Logistic Regression**, **Random Forest**, and **Deep Learning models** are used to predict employee turnover, providing actionable insights for retention strategies. The Marketing Department leverages **K-Means**, **PCA**, and **Autoencoders** for customer segmentation, enabling more targeted campaigns. In Sales, **time series forecasting** with **Facebook Prophet** helps predict daily sales, ensuring efficient inventory management. The Operations Department employs **deep learning models** for chest disease detection, enhancing medical diagnostics. Finally, the Public Relations Department applies **Naive Bayes** and **Logistic Regression** to predict customer satisfaction and improve engagement strategies.