Big Mart Sales Prediction (KEY IJP************146)

• Gowtham S,Swamydoss D

Abstract

Sales prediction is a critical aspect of retail business management, enabling companies to anticipate customer demand, optimize inventory levels, and plan marketing strategies effectively. In this abstract, we explore the application of Python programming and machine learning techniques to predict sales for Big Mart, a fictional retail chain, using historical sales data and other relevant features.The process begins with data preprocessing, where historical sales data is cleaned, transformed, and prepared for analysis. Python's Pandas library is instrumental in this step, providing powerful tools for data manipulation and exploration. Feature engineering is also employed to create new features that capture important patterns and trends in the data, such as store size, location, and promotions.With the data prepared, various machine learning algorithms are applied to build predictive models. Common algorithms include linear regression, decision trees, and ensemble methods like random forests. These models are trained on historical sales data along with the engineered features to learn patterns and relationships that can be used to predict future sales.Python's scikit-learn library offers a wide range of tools for model evaluation and validation, allowing analysts to assess the performance of their models and fine-tune them for better accuracy. Additionally, data visualization libraries like Matplotlib and Seaborn can be used to visualize the data and model predictions, helping to interpret the results and communicate findings effectively.