Optimizing sales strategy in the Indian automobile industry: Predicting future car prices using machine learning and demographic data.

Demographics play a vital role in defining the size, distribution, and structure of a population. In the context of the automobile industry, business owners can leverage demographic insights to gauge the demand for vehicles and strategically align their sales efforts. Accurate sales forecasting is essential for long-term business strategy, providing manufacturers with a competitive advantage in optimizing production planning methods. This project utilizes large-scale automobile sales data to forecast car price variations in the coming months, considering factors such as purchase patterns, car models, and other relevant data. By analyzing different attributes from a past-year dataset, three machine learning algorithms: Linear Regression, Decision Tree Regression, and Random Forest Regression were employed to predict future car prices. The performance of each algorithm is evaluated using the R-squared value. Notably, the Random Forest regression model achieves a higher accuracy of 93%, outperforming both Decision Tree regression and Linear regression. These results demonstrate the suitability of Random Forest regression in predicting big data for the industry's future product production plan and overall strategy. [ABSTRACT FROM AUTHOR]