/flight-price-prediction-flask

Primary LanguageJupyter Notebook

End to End Machine Learning Project

Build an end-to-end machine learning project. Deployed on render

Project Details

Learning Curves and Model Evaluation

  • Understanding Learning Curves: We explored how learning curves can be used to identify underfitting and overfitting in models.
  • Model Selection: We used learning curves to evaluate various models and select the best-performing one for our flight price prediction task.

Web Application Development

  • Flask: We built a web application using the Flask framework.
  • WTForms: We integrated WTForms for handling web forms and input validation.
  • HTML Templates: We utilized HTML templates with Jinja2 for dynamic content rendering.
  • Template Inheritance: We applied template inheritance to create reusable and maintainable templates.

API Serving and Deployment

  • Model Serving: The trained machine learning model was served as an API using Flask.
  • Deployment: The final Flask application was deployed using Render, making the web app accessible online.