Tutorial : https://www.datascience2000.in/
Download Trained model : https://drive.google.com/file/d/1D316GN33xrBNftF1zDux28vwD6ssniL0/view?usp=sharing
In this tutorial, we are going to implement the Flight Fare Prediction. This model predicts the price of the flight based on some parameters like total stops, journey Day, journey month, Air India, Indigo, source, destination, etc. I have trained this model using the random forest regressor and after training, fine-tune the model which is also known as hyper parameter tuning. Then save a model and deploy this Flight Fare Prediction model using the Flask application on the localhost.
We have a data set of flight fares in two Excel files, one is a training file and another one is a testing file. Training dataset contains 10683 rows and 11 columns. Test dataset contains 2671 rows and 10 columns.