This repository consists of files required for end to end implementation and deployment of Machine Learning Airline Fare Prediction web application created with Flask and deployed on the Railway platform.
- Python Version: 3.7 or above (I have used python 3.11.0 on localhost and 3.7.1 on Railway host)
- Packages: Flask, sklearn, pandas, numpy, matplotlib, seaborn, gunicorn, scikit-learn, pickle, datetime
- For Web Framework Requirements: pip install -r requirements.txt
- Dataset: https://github.com/Kritik007/Airline-Fare-Prediction/blob/main/Airline_Fare_Prediction_Data.xlsx
- Flask Productionization
- Linear Regression, Ridge, Lasso
- K-Neighbors Regressor
- Decision Tree Regressor
- Random Forest Regressor
- GridSearchCV