Sarika N. Kamble,
Rutuja Kanse,
Rushil Jangam,
- Assistant Professor, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
- Student, Department of Electronics & Telecommunication Engineering, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
- 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. ML calculations show execution contrasts relying upon the dataset and handling steps. Picking the right calculation, preprocessing and post handling techniques has incredible significance to accomplish great outcomes. The Random Forest Classifier, K-Nearest Neighbor Classifier, and SVC methods are evaluated to forecast mobile phone price categories. The “Prediction” dataset which is taken from Kaggle.com is utilized to assess strategies. First Dataset is verified for it then clean the dataset. From that point onward, scaling is applied to dataset to get more important information for ML calculations. Then, highlight choice strategies which 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 SVC classifier, Gradient Boosting has the most accurate results than other 4 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 (ctit)]
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):-.
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):-. Available from: https://journals.stmjournals.com/ctit/article=2024/view=171694
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Current Trends in Information Technology
| Volume | 14 |
| Issue | 03 |
| Received | July 3, 2024 |
| Accepted | July 26, 2024 |
| Published | September 11, 2024 |