Predictive Modeling of Hair Fall Using Random Forest Algorithms

The study presents a predictive model for hair loss using Random Forest algorithms, highlighting its superior performance compared to other models like Logistic Regression, SVM, KNN, Gradient Boosting, and XGBoost. The model considers factors such as heredity, hormonal imbalances, medical conditions, medications, dietary deficiencies, stress, and lifestyle choices to predict hair loss patterns. Key techniques include label encoding, handling missing values, feature scaling, and hyperparameter tuning with GridSearchCV to enhance accuracy. A Django web interface allows users to input data and receive real-time predictions, demonstrating the potential of machine learning and predictive analytics in dermatology. Future improvements aim to increase accuracy through data enrichment, model tuning, and deep learning.