Machine Learning Algorithms for Prediction & Analysis of Car Sales Record of Magic Auto Sales in Little Ferry, New Jersey, New York

Abstract

Forecasting auto sales is essential for improving operational effectiveness and decision-making in the automotive sector. By examining findings from recent research, this paper investigates the use of machine learning algorithms for auto sales prediction. This study demonstrates how well machine learning methods—such as ensemble approaches, neural networks, and regression models—predict sales trends. The study examined important variables such as car type, cost, and local demand using real-world statistics and found noteworthy trends in consumer buying patterns. According to the findings, mid-range cars and SUVs routinely top the sales charts, and pricing and special offers have a big influence on consumers’ decisions to buy. Results showed that mid-range sedans and SUVs are often the best-selling vehicles and that price and special offers have a big influence on people’s decisions to buy cars in the region of Little Ferry, New Jersey, New York. Demand distribution was depicted using visual aids like heatmaps and bar charts, and the top ten best-selling models were found, providing manufacturers and dealers with useful information. In order to maximise inventory, minimise overproduction, and satisfy consumer demand, the findings highlight how crucial it is to include machine intelligence in auto sales forecasting. To create more reliable predictive models, future studies should examine other elements like seasonal patterns and economic data.