Machine Learning Zoomcamp at DataTalks.Club

 Predicting Patient No-Shows: A Data-Driven Approach Hospital no-shows significantly disrupt healthcare systems, wasting resources and delaying care for those in need. My midterm project for the MLZoomcamp , led by Alexey Grigorev and hosted by DataTalksClub, tackles this challenge using machine learning to predict no-show probabilities for appointments in Brazilian hospitals. Here's how I approached the problem: The Challenge The dataset, sourced from Kaggle, includes over 110,000 appointments and diverse features such as patient demographics, appointment details, and medical history. However, achieving reliable predictions is complex due to: Imbalanced Data : About 80% of appointments were attended, while 20% were no-shows. Dependence on Feature Engineering : Key predictors like patient history (previous/missed appointments) were engineered from the raw data. Bias Mitigation : Socioeconomic factors, such as neighborhood, required careful handling to ensure fairness. The Solution ...

In this chapter of the ML Zoomcamp by DataTalks.Club (led by Alexey Grigorev), we dived into Decision Trees and Ensemble Learning —two core components in supervised machine learning that offer high interpretability and flexibility. This chapter addresses decision trees, their structure, splitting methods, as well as ensemble techniques like bagging, boosting, and stacking to improve model performance. Notable briefings on the same are as follows: Decision Trees: Core Concepts and Learning In this section, the course covers decision trees as intuitive, rule-based algorithms that are effective yet prone to overfitting on complex datasets. Key topics include: Splitting Criteria:  Decision trees divide data by optimizing splits to minimize classification error. Concepts like "impurity" are introduced, helping learners understand how criteria such as Gini impurity and entropy guide the algorithm in choosing splits that reduce classification mistakes. Overfitting risks are discu...