Tekstil sektöründe perakende satış analizi ve tahminlemesi

This thesis addresses sales forecasting in the retail domain using machine learning methods. Nowadays, the retail sector emerges as a rapidly changing and exponentially growing field. Therefore, obtaining accurate and reliable predictions in sales forecasting holds critical importance for businesses to sustain their competitive advantage. Machine learning is recognized as an effective tool with data analysis and pattern recognition capabilities to address complex problems like sales forecasting. This study aims to tackle the sales forecasting problem in the retail sector and investigate how machine learning methods can be utilized to solve this issue. Firstly, an exploration of the existing sales forecasting methods in the literature and machine learning algorithms is conducted. Subsequently, the effectiveness and performance of various machine learning algorithms (such as LGBM, LSTM, XGBoost) in sales forecasting are compared. This thesis is supported by experimental studies conducted on real-world datasets. The datasets encompass sales data from the textile retail sector and comprise data obtained over a specific time frame. Analyses conducted on these datasets reveal how machine learning algorithms can offer an advantage in sales forecasting compared to traditional methods. The results demonstrate that machine learning algorithms constitute an effective and accurate tool for sales forecasting in the retail sector. This thesis provides valuable insights into the LightGBM method specifically chosen and can aid businesses in enhancing strategic decisions, such as demand management, inventory optimization, and stock planning.