Prediction of customer behavior using machine methodology

The main purpose of this study is to use machine learning methods for predicting customer behaviors. This represents an extremely important objective as it helps businesses enhance their ability to understand and analyze customer behaviors. Predicting customer behaviors is crucial for businesses in terms of determining marketing strategies, improving products and services, and increasing sales. This thesis focuses on an application that aims to predict customer behaviors using an open-source dataset. The dataset consists of more than 10,000 data entries, representing a rich source of data. Analyses on this large dataset were conducted using deep learning methods, which are part of the field of machine learning. In the application, significant machine learning algorithms such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN) were employed. These algorithms are powerful learning methods used to understand and predict the complexity and structures of the dataset. The programming environment used for this study is Python. According to the results of the study, the Long Short-Term Memory (LSTM) method yielded better prediction results compared to the Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods. This indicates significant potential for providing businesses with a better understanding of and insight into predicting customer behaviors. In conclusion, this thesis demonstrates how machine learning and deep learning techniques can be employed to enhance businesses' capabilities in predicting customer behaviors. This can potentially help businesses gain a competitive advantage and provide better services. Moreover, it underscores the potential for further research and development in the fields of machine learning and deep learning. Such studies could contribute to the success of customer-centric businesses in the future.