Linear Regression Algorithm in Pulse Purchase System Simple Using Python

Abstract

In today's digital era, the online credit purchase system has become an integral part of everyday life. The use of linear regression algorithms in this context is becoming increasingly relevant, as it provides a powerful approach to analyzing and predicting pulse buying patterns. This research proposes a simple pulse purchase system that implements a linear regression algorithm, using the Python programming language. The purpose of this study is to develop a predictive model that can estimate the amount of credit to be purchased based on certain variables, such as the time of purchase, the number of previous transactions, and the value of prior purchases. By analyzing historical transaction data, the system can take into account possible purchase patterns and estimate future credit needs with an adequate level of accuracy. The implementation of linear regression algorithms in Python allows users to easily access and use this pulse purchase system. Through a simple but intuitive interface, users can enter their transaction parameters and the system will predict the required number of pulses. Experiments were conducted to test the performance of this system in producing accurate predictions. The results of the experiment show that this system can provide estimates close to the real value, with a high degree of accuracy. This indicates that the use of linear regression algorithms in pulse purchase systems has great potential to improve efficiency and reliability in online transactions. In addition, the implementation of this algorithm also has a positive impact on transaction security. By analyzing purchasing patterns, the system can detect anomalies or suspicious activities that may occur, thereby increasing the level of security in the process of buying credit online. Overall, this study shows that the use of linear regression algorithms in pulse purchase systems has significant benefits in improving the efficiency, reliability, and security of online transactions. The practical implementation of this algorithm in the Python programming language opens the door for further development in the analysis and optimization of future pulse purchase system.