Predictive Analytics in Retail: Revealing the Strategic Impact of Advertising Channels on Sales Performance Through Python and Linear Regression Model

This research paper presents a comprehensive sales prediction model tailored for the retail industry, specifically focusing on diverse products within this sector. Leveraging advanced machine learning techniques such as Linear Regression and Random Forest Regression, the model assesses the nuanced impact of various advertising channels, with a particular emphasis on television (including a few social media like YouTube advertisements and Mobile Applications like Disney + Hotstar, SunNxt, etc.), radio, and newspaper mediums. Notably, the findings emphasize the pivotal role of TV advertising in driving sales, offering strategic guidance for resource allocation and marketing strategies. This research contributes to the enhancement of decision-making processes within the retail industry, empowering stakeholders to optimize marketing approaches and navigate the dynamic landscape with confidence and precision.