shreyagarg021/airfoil-regression-with-deployment

Airfoil-Regression-With-Deployment

Airfoil regression using random forest is a machine learning model that predicts the lift coefficient of an airfoil based on various parameters such as its angle of attack, airspeed, and other physical characteristics. The model is trained on a dataset of known airfoil characteristics and their corresponding lift coefficients.

To build a front-end for the airfoil regression model using Flask, we would need to create a user interface that allows users to input the various airfoil parameters and view the predicted lift coefficient. This can be achieved using HTML, CSS, and JavaScript, which are commonly used for web development.

Once the front-end is built, the next step is to integrate it with the airfoil regression model. This can be done using a Python script that loads the trained model, receives the user inputs from the Flask app, and returns the predicted lift coefficient. The random forest algorithm can be implemented using Python's scikit-learn library.

Once the application is ready, it is deployed on Heroku, which provides a scalable and reliable platform for hosting web applications. This can be achieved by creating a Heroku account, creating a new application, and deploying the Flask app to the Heroku server using Git.