Paper Title
Deep Learning-Based Assessment and Prediction of Diwali Sales Using a Personalized Composite Loss Function

Abstract
The festive season retail analytics process faces difficulties because it needs to handle two factors which involve increased transaction volume together with customer behavior changes while using demographic data and product attribute connections to perform data analysis. Retailers who can correctly define customer groups and forecast their buying behavior will achieve better sales results and improved business decisions. The study presents a comprehensive machine learning system which utilizes representation learning to create clusters and make predictions about Diwali retail transactions. The team processed the Diwali Sales dataset through cleaning and preprocessing steps to eliminate nonessential features and treat missing data and transform categorical data into numerical format. The deep autoencoder learned to produce compact latent representations from high-dimensional retail transaction data. KMeans clustering used the learned embeddings to divide customer transactions into different segments. Principal Component Analysis (PCA) produced a two-dimensional projection of the embeddings which allowed researchers to verify cluster existence while they studied the latent space structure. The research team built multiple regression models which included Linear Regression and Random Forest Regressor and Long Short-Term Memory (LSTM) and a hybrid model that combined autoencoder embeddings with Linear Regression to predict transaction amounts. The experimental results indicate that the autoencoder successfully captures essential patterns in customer purchasing behavior which enables continuous transaction grouping. The predictive modeling phase shows that machine learning models achieve successful prediction of purchase amounts through customer and product characteristic analysis which provides essential data for holiday retail assessment. The proposed framework shows that deep representation learning combined with traditional machine learning methods can effectively analyze retail data. Keywords - Diwali Sales Analytics, Autoencoder, Customer Segmentation, KMeans Clustering, Retail Purchase Prediction, Machine Learning in Retail, Representation Learning