A computer system that facilitates information sharing and transmission among various computing devices is known as a networked computer. The computer networks used for traffic monitoring in cities represent a large-scale network of computer. Marketing Mix Modeling (MMM) is a potent statistical technique that optimizes the distribution of future marketing budgets across channels by measuring the effect of different marketing activities on a key performance indicator (KPI), such as sales or customer lifetime value (LTV), using historical data. Measuring the effect of specific marketing campaigns on sales has grown increasingly difficult as modern marketing becomes more complicated and consumers interact with firms through a variety of channels and devices. Because of this, marketers are now using MMM to protect client privacy while also better understanding how their marketing mix is influencing sales. The retail sector has realized that it must move toward data-first marketing in an age when data has supreme power. The client faced significant marketing issues in the absence of a well-defined data strategy, one of which was a sharp fall in a crucial success parameter: customer experience. It is still unclear what effect spending on advertisements in a variety of media—TV, radio, Google Ads, print promotions, newspaper ads, Special Day promotions, and digital channels—would have. This ambiguity resulted in poor marketing strategies, which negatively impacted sales. Marketing Mix Modeling has the power to gain a deeper comprehension of the connections that occur between the target metric—conversions—and the marketing channels. Filtering preliminary information, or analyzing exploratory information, is a type of information filtering that helps reveal hidden patterns, spot possible anomalies, and prepare the ground for well-informed decision-making. An exploratory marketing mix model (using just user interfaces!) helps in forecasting future sales volume based on future marketing and price strategies. In this work, we understand the need of Marketing Mix Modeling in predicting the impact of every marketing channel used by a certain business and performs exploratory data analysis on a certain dataset. This work explains the primary requirement for deploying the computer network, data analysis process, the stages of exploratory data analysis (EDA), and their importance in day-to-day life. It entails going over the data to find trends, spot abnormalities, and comprehend the features of the data on a deeper level. EDA aids in preparing the data for additional analysis and modeling, guaranteeing accurate and significant outcomes.