Accurate Estimation Technique Development for Second Hand Car Through Linear Regression Algorithm

Car production volumes have increased dramatically over the past decade, reaching 92 million vehicles produced by 2019. This increase has particularly boosted the used car market, emerging as a growing industry thanks to the internet so fashionable methods Need to stay current market trends for determining real value have increased in many applications of machine learning, its use in predicting problems stands out in the way in a remarkable way. This work focuses on the use of machine learning algorithms, in particular linear regression, to predict used car prices and to develop mathematical models using data with specific characteristics. In this period marked by rapid growth, our research is at the forefront of a new approach to car price forecasting. Our solution, using machine learning techniques, aims to provide comprehensive support, significantly saving time and costs associated with car pricing calculations. Forecasting the price of a car is a major one, requiring a great deal of expertise and thorough diligence. We thoroughly examined a variety of factors to make accurate and reliable predictions. We used Linear Regression, a leading machine learning technique, to develop a +model to predict used car prices. This method incorporates multiple independent variables and one dependent variable, so that actual and predicted values can be compared to assess forecast accuracy Our study uses a system with a forced dependent variable on the cost depending on factors such as mileage, year of production, and variety come other characteristics.