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Utilized Spark CLI to predict Flight Price using Regression models and measured the accuracy of models by calculating the Root Mean Square Error and R Square.

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prati-y/DataScience-Project-Predective-Analysis-On-Flight-Ticket-Pricing

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CIS5560 PREDICTIVE ANALYSIS ON FLIGHT TICKET PRICING DATA

Airline datasets can be used to predict and determine the price of different airlines, helping airlines optimize their pricing strategies for specific routes and seasons.​

Analyzing flight price datasets enables the identification of pricing trends over time, which is crucial for airlines in developing effective pricing strategies.​

By leveraging these insights, airlines can determine the most optimal pricing approach for specific routes and periods.​

The dataset can also benefit customers by allowing them to forecast future flight prices, helping them plan their journeys accordingly.​

We use machine learning models to perform predictive analysis on Flight Pricing ticketing data.​

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Utilized Spark CLI to predict Flight Price using Regression models and measured the accuracy of models by calculating the Root Mean Square Error and R Square.

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