Project Overview Analyzed 110K+ patient appointment

Project Overview Analyzed 110K+ patient appointment records to identify key demographic and behavioral drivers behind clinic attendance and no-shows. Designed an interactive Power BI dashboard tracking a 71.48% baseline attendance rate across 71.95K+ recorded clinic visits. ​Key Insights & Dashboard Features ​Demographic Drivers: Identified attendance trends across age groups (average patient age: 38.50 years) and gender distribution (66.8% female vs. 33.2% male). ​SMS Reminder Impact: Evaluated notification efficiency, revealing higher attendance rates among patients receiving SMS reminders compared to non-recipients. ​Geographic & Chronic Risk Profiling: Mapped top neighborhoods with highest no-show counts and evaluated attendance correlation with chronic conditions like Diabetes and Hypertension. ​Predictive Modeling: Built machine learning classification models (CatBoost, XGBoost, SMOTE) in Python to predict high-risk no-shows and optimize reminder schedules. ​Tools & Technologies ​Data Processing & ML: Python (Pandas, Scikit-learn), SMOTE, XGBoost, CatBoost ​Visualization & BI: Power BI, DAX, Power Query

you can see the job here[https://www.kaggle.com/code/mohamedmoustfaghanem/hospital-operations ]