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Tractor Sales Forecast Using Machine Learning
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, Özgür
This study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.

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