Browsing by Author "Muhammad Shoaib Qureshi, Muhammad Faique and Muhammad Adnan Ayub"

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    Flight Fare Price Prediction A Time Series Forecasting
    (UMT, Lahore, 2023) Muhammad Shoaib Qureshi, Muhammad Faique and Muhammad Adnan Ayub
    Time series a data science specialization is a collection of observations of well-defined data items obtained through repeated measurements over time. A time series is a sequence of data points that occur in successive order over some period of time. However, in the field of data science, data can be of numerous types. It is a fare ticket price prediction/forecasting of airline flights. This is also involving regression problems. We have predicted prices of different airlines based on date, departure date and arrival date and airline name. Dataset can be numeric, or timestamp type. Data in which mainly time is involved such as a data with the timestamp along with day, month or year. Based on hourly, daily, monthly and even yearly. Airlines flight prices prediction is a data science product-based final year project. To solve problems for people who want to book seats for airlines that will eliminate the need for them to do their own research for acknowledgment of upcoming future prices for flights booking. People cannot plan their schedule accordingly for flights because of the fear of unknown future prices. We have implemented different models of machine and deep learning like ARIMA, SARIMA, Facebook Prophet Model, Regression models, Random Forest models, and many other best etc. It is written in python. Our web-based platform provides a great accurate prediction of price for flight fare tickets. Hence, it has a potential to give opportunities to passengers to buy tickets timely according to their budget and schedule. It is practical. An end user will give multiple inputs to forecast/predict fare price for a particular airline and dates of the flights. In result, gets the output predicted flight fare price for the future for that particular airline.