AliaAbdullah727/healthconnect-no-show-prediction

Predicting patient appointment no-shows using healthcare data and machine learning | AnalystLab Africa Experience Lab # 🏥 HealthConnect: Patient No-Show Prediction ## Project Overview HealthConnect is a healthcare data science project developed as part of the **AnalystLab Africa Experience Lab Internship Programme**. The project explores how data and machine learning can help HealthConnect Clinic understand and reduce missed patient appointments. The main Data Science objective is to determine whether information available before an appointment can be used to predict the risk of a patient becoming a **no-show**. This repository will develop across multiple stages, starting with problem understanding and eventually moving into machine learning development, testing, and refinement. --- ## Business Problem Missed appointments can leave appointment slots unused and make clinic planning less efficient. HealthConnect Clinic wants to explore whether machine learning can help identify appointments with a higher risk of becoming no-shows. A future prediction system could support actions such as targeted reminders and administrative follow-up. The model is intended to support decision-making. It is not intended to make medical decisions. --- ## Machine Learning Problem The proposed task is a **supervised binary classification problem**. The model will aim to predict: - `0` → Attended - `1` → No-Show The main question is: > **Can information available before a scheduled appointment be used to predict whether the patient will attend or become a no-show?** --- ## Dataset The HealthConnect Appointment Dataset contains: - **5,000 appointment records** - **18 variables** - **1,696 unique patients** The dataset includes information about: - Patient demographics - Appointment details - Booking information - Previous appointments - Previous no-shows - Appointment reminders - Distance to the clinic - Estimated waiting time - Appointment outcomes --- ## Initial Data Assessment The dataset was reviewed for: - Missing values - Duplicate records - Date consistency - Appointment …