FMCG Market Analysis for Wholesalers and Retailers Using Machine Learning

The fast-moving consumer goods (FMCG) industry is one of the largest and most dynamic industries globally, comprising a vast range of products that are sold fast and at relatively low cost. FMCG products are typically purchased frequently and in large quantities by consumers, making it an essential market for both retailers and wholesalers. To understand the FMCG market trends and consumer behavior, machine learning techniques and statistical analysis were utilized, including the use of Python libraries such as Pandas, Seaborn, Matplotlib, and Mlxtend. The methodology adopted involved data collection, data preprocessing, and data analysis using various machine learning algorithms. The data collection process involved gathering information from different sources, including online databases, market research reports, and surveys. After gathering the data, it was pre-processed to ensure data precision, reliability, and thoroughness. The processed data was then examined using various machine learning algorithms, such as linear regression, decision trees, and random forests. FMCG market analysis is highly significant due to its capacity to evaluate large and complicated datasets, detect trends, and predict future developments. Moreover, the literature survey highlighted the significance of FMCG market analysis in identifying consumer behavior and predicting market trends, which is vital for retailers and wholesalers to make knowledgeable choices. The outcomes achieved from the examination suggest that machine learning strategies can accurately anticipate FMCG market trends, cus-tomer behavior, and sales patterns. The analysis also revealed that product excellence, cost, and promotions significantly impact customer purchasing behavior in the FMCG sector