Smartphones price estimation System

ABSTRACT:

The Smartphone Price Estimation System aims to predict the price of smartphones based on various features such as brand, model, specifications, and other relevant factors. The system leverages machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and XGBoost, to generate accurate price predictions. The dataset used in the project is sourced from Kaggle and includes a variety of Samsung mobile devices. By analyzing key attributes such as battery life, camera quality, screen size, and processing power, the system provides an estimated price for each device. The front-end interface is developed using HTML, CSS, and JavaScript, ensuring a user-friendly experience, while the back-end is powered by Python to handle data processing and machine learning model integration. This project enhances the decision-making process for users looking to purchase smartphones by providing real-time price estimations based on the latest market data.

Keywords: Smartphone, Price Prediction, Machine Learning, SVM, Random Forest, XGBoost, Data Analytics, Front-end, Back-end, Python