A New Lens on Domestic Fare Pricing Through Cloudfare

This project, “A NEW LENS ON DOMESTIC FARE PRICING THROUGH CLOUDFARE”, is a web application built using Python (Flask) and MySQL ssto predict domestic flight costs. A Random Forest model, trained on a dataset of historical flight costs and relevant travel data, forms the core of the prediction engine. The application provides a user- friendly interface for registered users to input travel details (origin, destination, dates, Flights Company). Cloud Fare then leverages the trained model to estimate flight ticket costs. An integrated feature allows users to check weather conditions at both departure and arrival cities, providing valuable context for travel planning, though weather data is not directly incorporated into the flight cost prediction model at this time. Future development will focus on enhancing the model's accuracy by incorporating additional features, including weather data and potentially other relevant economic indicators. Keywords: Cloud Fare, Flight Ticket Prediction, Domestic Travel Costs, Python Flask, Random Forest Algorithm, Machine Learning Model, Flight Cost Estimation, Travel Data Analysis, User Authentication, Weather Integration, Flight Price Prediction Engine, Travel Planning Tool, MySQL Database, Web Application, Historical Flight Costs, Data-Driven Insights, Graphical User Interface (GUI), Origin and Destination Prediction, Real-Time Weather Check, Future Enhancements, Economic Indicators, User-Friendly Design, Travel Decision-Making.