| Sumario: | The expansion of branch networks of large retail chains, the growth of their customer base, and the increasing diversity of customer profiles are exacerbating the complexity of sales forecasting processes. Managing this diversity and its implications presents a significant challenge for retailers in terms of both strategic planning and operational implementation. At this point, developing customer segmentation and personalized marketing strategies, determining unique approaches for each customer group, and effectively managing this diversity are becoming increasingly crucial. The emerging technologies, particularly machine learning methods, present the potential to cope with these challenges. In light of this, the main objective of the research is to perform sales forecasting on a retail company's Black Friday sales data using machine learning algorithms named Linear Regression, Random Forest Regression, K-Nearest Neighbors Regression, XGBoost Regression, Decision Tree Regression and LightGBM Regression and determine the best performing algorithm by comparing their performances. It is also aimed to tune the hyperparameters using GridSearchCV and examine the effect of these adjustments on the performance of the models. Additionally, Exploratory Data Analysis will be conducted on the dataset to create a sample example for businesses in the retail sector on how they can extract useful information from their available data and effectively evaluate it. According to the results obtainedfrom the research, the most successful algorithm in predicting sales was the XGBoost Regression with hyperparameters tuned using GridSearchCV. It has been determined that the majority of the company's customers consist of individuals aged 26-35, with male customers making significantly higher purchases compared to females and single customers spending more than married ones. Furthermore, when examining the average amount of purchases made by each age group, it was identified that those within the range of 51-55 years had the highest average spending rate.
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