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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Airline fare prediction using machine learning |
| Authors Name: | Dr. S. Prem Kumar , Shaik Mohammad Ashfaq , R.N Ramu , Pinjari Mohammad Rafiq , Chitiveli Neeraj Varma |
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IJRTI_187177
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| Published Paper Id: | IJRTI2306051 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | Airline fare prediction plays a crucial role in enabling travelers to make informed decisions while planning their journeys. This study presents a data-driven approach utilizing the Random Forest algorithm to forecast airline fares accurately. By leveraging historical flight data, including various attributes such as departure and arrival locations, travel dates, airlines, and other relevant factors, we aim to develop a reliable model for fare prediction. The methodology employed involves several stages. Firstly, a comprehensive dataset containing a diverse range of flight records is collected, processed, and pre-processed. Feature engineering techniques are applied to extract meaningful insights from the data, ensuring the inclusion of relevant factors that impact fare fluctuations. Subsequently, the dataset is partitioned into training and testing sets to train the Random Forest model.The Random Forest algorithm is a powerful ensemble learning technique that combines multiple decision trees to make predictions. It provides robustness against overfitting and handles both numerical and categorical data effectively. Through an iterative process, the model is trained on the training set, and its performance is evaluated using various evaluation metrics such as mean absolute error and root mean squared error.To enhance the accuracy of the predictions, hyperparameter tuning is performed on the Random Forest model. This process involves systematically searching for the optimal values of model parameters using techniques such as grid search or random search. The best-performing model is then selected based on the evaluation results.Experimental results on real-world flight data demonstrate the efficacy of the proposed approach in predicting airline fares. The Random Forest model achieves high accuracy and generalizability, outperforming baseline models and showcasing its potential as a valuable tool for fare prediction. The insights derived from the model can assist both travelers and airline industry stakeholders in making informed decisions, optimizing pricing strategies, and improving overall customer satisfaction .In conclusion, this study presents an effective approach for airline fare prediction using the Random Forest algorithm. By harnessing the power of data and machine learning, it offers a practical solution to address the complex nature of fare dynamics in the aviation industry. The findings contribute to the advancement of fare prediction methodologies and hold significant implications for various stakeholders in the air travel ecosystem. |
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| Cite Article: | "Airline fare prediction using machine learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.318 - 322, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306051.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2306051
Registration ID:187177
Published In: Volume 8 Issue 6, June-2023
DOI (Digital Object Identifier):
Page No: 318 - 322 Country: Kurnool, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2306051 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2306051 |
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ISSN: 2456-3315
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