Optimised Laptop Price Prediction

Abstract

We are presenting the optimized price prediction of laptops in this paper using supervised machine learning techniques. The prediction precision is up to 81% in this research with the usage of the machine learning prediction method (multiple linear regression techniques). There are multiple independent variables when using multiple linear regressions but only one and single dependent variable, the actual value of the dependent variable is compared with the predicted value of the dependent variable to know and find result precision. This paper proposes a system where the price is the dependent variable which is predicted, and this price is predicted by taking some input values from the user like Company, Laptop type, RAM, Weight, Touch Screen, IPS, Screen Size, Resolution, CPU, ROM (HDD/SSD), GPU, Operating System. In today's world, everything is getting costly day by day, especially electronic things and people are not able to afford these costly things generally but we have an alternate option i.e., to buy an item with good research which belongs to their requirement in pocket-friendly range. So, this project is solving a research issue for the user and users can optimise the price of a Laptop. Artificial intelligence is advancing day by day so we take the help of AI to make our project more accurate. This project uses a statistical technique that uses two or more independent variables to predict the outcome of a dependent variable i.e. Multiple linear regressions and a regression Model provide a function that describes the relationship between one or more independent variables and a response, target variable. Machine Learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values.