This project aims to classify mobile phones into different price ranges using various machine learning algorithms and feature selection techniques.
Smartphones have become a necessary part of our daily lives, and the cost of these devices is an important factor that customers consider while making a purchase. The objective of this study is to build a model that can accurately classify a cellphone into a certain price range and determine its class. Machine learning provides the best techniques for artificial intelligence, such as classification and regression, and can be used to build a model that accurately predicts the price range of a mobile phone.
Feature Selection Techniques In this project, four different feature selection techniques were used to identify and eliminate features that are less necessary and have low computational complexity. These techniques are:
- LASSO
- Boruta
- Recursive Feature Elimination
- Variable Importance Method
These techniques were used to identify the most important features that affect the price range of a mobile phone.
Various machine learning algorithms were used to classify mobile phones into different price ranges. These algorithms include:
- Multinomial Logistic Regression
- K-Nearest Neighbors (KNN)
- Naive Bayes Classifier
- Classification and Regression Trees (CART)
- Support Vector Machines (SVM)
- Random Forest Classifier
These algorithms were used to train the model and determine the accuracy of the classification.
This project provides a model that can be used to classify mobile phones into different price ranges accurately. The results of different feature selection techniques and machine learning algorithms were compared, and the best performing algorithm was identified. The project can be used in any business strategy to find the corresponding product with minimum and affordable cost.
To contribute to the project, you can open a pull request with your changes.
If you have any questions or issues, feel free to reach out to me at [email protected].