This data science project aims to classify mobile phones into different price ranges using various machine learning algorithms and feature selection techniques such as LASSO, Boruta, and Recursive Feature Elimination.

Perform exploratory data analysis (EDA) to gain insights into the relationships between different features and the target variable (price range). Select appropriate machine learning algorithms for classification and evaluate their performance using suitable metrics.

Mar 22, 2022 · In this article, you will learn about mobile price prediction using four different classification algorithms.

In this case, this article aims to solve this problem by using machine learning algorithms such as Support Vector Machine, Decision Tree (DT), KNN, and Naive Bayes to train the mobile phone dataset and make price predictions. We used appropriate algorithms to predict smartphone prices based on accuracy, precision, recall, and F1 score.

We used appropriate algorithms to predict smartphone prices based on accuracy, precision, recall and F1 score. This not only helps customers have a better choice on the mobile phone but also gives advice to businesses selling mobile phones that the way to set reasonable prices with the different features they offer.

Mobile price classification is a common task in the field of machine learning where the goal is to build a predictive model that can classify mobile phones into different price ranges based on their features and specifications.

Mar 24, 2018 · This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model.

The dataset provided for this project contains information about technical characteristics of mobile phones as well as price ranges. The objective is to analyze the device characteristics...

To classify and estimate the price range of a mobile phone, this study maneuvers five machine learning (ML) techniques: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-nearest neighbors (KNN).

Mar 5, 2021 · In the section below, I will introduce you to a machine learning project on a mobile price classification model where I will train a model to classify the price range of mobiles using Python. This task is based on solving a case study mentioned below.