Intelligent Classification of Migraine Types and Mobile Price Prediction: A Practical Django-based Machine Learning Web Application

This project demonstrates a fully functional Django machine learning web application that integrates two practical features: migraine type classification and mobile price prediction. It covers the entire workflow of user authentication, interactive UI, and model deployment, serving as an excellent example for beginners to develop end-to-end ML applications. The project includes two classic machine learning problems—classification (migraine types) and regression (mobile prices)—making it an ideal resource for learning full-stack ML development.

Migraine is a neurological disorder affecting approximately 1 billion people worldwide. Accurately classifying its subtypes (without aura, with aura, chronic, etc.) is crucial for treatment. Mobile price prediction is a typical regression problem in e-commerce and other scenarios, which requires predicting prices based on hardware specifications, brand, and other factors. This project targets these two practical scenarios to build ML solutions and deploy them as web services.

Migraine Classification: Multi-class classification problem. Input features include headache location, pain nature, accompanying symptoms, aura type, etc. Optional models include Random Forest, SVM, or Gradient Boosting. Evaluation focuses on classification accuracy and confusion matrix.

Mobile Price Prediction: Regression problem. Features include hardware specifications (CPU performance, memory, screen parameters, etc.), brand premium, release year, etc. Data preprocessing includes category encoding, numerical normalization, and outlier handling. Optional models include Linear Regression, Random Forest, XGBoost, etc. Evaluation uses RMSE, MAE, and R² scores.

Django was chosen because of its native integration with Python's ML ecosystem, rapid development (built-in ORM/forms/authentication), security protection, and scalability. System architecture: User request → URL routing → View processing → Model inference → Template rendering → Response. A complete user authentication system (registration/login/permission control) and interactive UI (responsive forms, highlighted result display, visualization) are implemented.

Model persistence uses pickle/joblib (for scikit-learn) or HDF5 (for deep learning), and version management is required. Inference optimization: Preload models at application startup, batch inference, lightweight model compression. Error handling: Input validation (range/missing values), exception catching, performance monitoring (prediction latency/distribution changes).

For beginners: It provides an end-to-end ML workflow perspective, helps learn web development basics and engineering practices, and understand user experience and real-time inference constraints. Expansion directions: Feature enhancement (adding disease screening, prediction interpretability), technical upgrades (FastAPI, Docker containerization), ML deepening (neural networks, online learning).

Current limitations: Low model complexity, limited scale of open-source data, and UI/UX needing optimization. Improvement directions: Data level (collect real data, data augmentation), model level (ensemble learning, hyperparameter tuning), system level (asynchronous task processing, caching mechanism, database optimization).

This project is an excellent example of full-stack ML development, covering key skills such as classification/regression problems, Django architecture, and model deployment. It has reference value for both ML engineers and web developers, reflecting the importance of engineering capabilities from model training to production services.