Big Mart Sales using Hybrid Learning Framework with Data Analysis

The retail industry has undergone a rapid transformation in recent years, owing to technological advancements and the availability of massive amounts of data. In this context, predictive analytics is critical in assisting retailers in making informed decisions, optimising operations, and increasing profitability. This study employs a hybrid learning framework combining machine learning and traditional statistical methods to predict sales in the Big Mart retail chain. The first stage of the proposed approach entails a thorough data analysis in which this study examines historical sales data, product attributes, store demographics, and other relevant variables to gain insights into sales patterns and correlations. Exploratory data analysis and feature engineering are used to prepare the data for model development. The new algorithm analyses customer data and provides insights to help businesses make better business decisions. Using this technology, Big Mart hopes to increase sales and improve customer satisfaction.Big Mart used the Ensemble algorithm in this paper, a type of artificial intelligence that can learn from data and make predictions based on that data. The ensemble algorithm is a hybrid algorithm that combines the two algorithms used to find Big Mart sales, such as Random Forest (RF) and Support Vector Machine (SVM). The algorithm is trained using historical customer data, such as purchase history, demographic information, and other variables that may influence purchasing behavior. The dataset is obtained from Kaggle to evaluate the model’s performance. The algorithm can use this data to identify patterns and trends in customer behaviour, which can be used to forecast future sales.