Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization

Year : 2024 | Volume : 14 | Issue : 03 | Page : 18 25
    By

    Sarika N. Kamble,

  • Rutuja Kanse,

  • Rushil Jangam,

  1. Assistant Professor, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  2. Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  3. Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India

Abstract

Machine learning calculations are utilized in many fields like money, training, industry, medication, and online business. Machine learning calculations show execution contrasts relying upon the dataset and handling steps. Picking the right calculation, preprocessing and post-handling techniques have incredible significance in accomplishing great outcomes. The Random Forest classifier, K-nearest neighbor classifier, and support vector machine methods are evaluated to forecast mobile phone price categories. The “prediction” dataset which is taken from kaggle.com is utilized to assess strategies. First, the dataset is verified and cleaned. From that point onward, scaling is applied to datasets to get more important information for machine learning calculations. Then, highlight choice strategies that lessen the computational expense by decreasing the quantity of data sources are performed to get significant elements. At long last, the boundaries of order calculations are tuned to further develop the framework exactness. After Feature selection accuracy of models got more with selected features. It gives fulfilling precision with a base number of highlights. It is likewise seen that the support vector machine and gradient boosting have the most accurate results than the other four models.

Keywords: Random forest classifier, gradient boosting, support vector machine, feature selection, parameter optimization, decision tree

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Sarika N. Kamble, Rutuja Kanse, Rushil Jangam. Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization. Current Trends in Information Technology. 2024; 14(03):18-25.
How to cite this URL:
Sarika N. Kamble, Rutuja Kanse, Rushil Jangam. Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization. Current Trends in Information Technology. 2024; 14(03):18-25. Available from: https://journals.stmjournals.com/ctit/article=2024/view=171694


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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 07/07/2024
Accepted 26/07/2024
Published 11/09/2024
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