A Machine Learning Approach for User Behavior Analysis in Developing Websites

In digit age, understand user behaviors with optimizing interactions on website crucial for achieve engaging and satisfactory. This study uses back propagation method for analyze user behavior pattern and utilize information for enhancement website design, include preprocess user data, convert category page names to numbers, and normalization usage time values. Neural network architecture simplified use, with input layer represent sequence of encoded pages, hidden layer extract feature, and output layer predict user behaviors. Neural networks trained use back propagation algorithm with sigmoid activation function. The dataset uses for training consist of an encoded sequence of pages and corresponding labels of user behavior. After training, the accuracy of network assesses using test data, and evaluate the effectiveness of forecasting. The information obtains from trained neural networks use for identifying influential pages and predict user behavior pattern based on interactions. The results indicate that average prediction accuracy based on test result be 75%. When analysis significance functions, pages stand out have significant impact on predict user behavior. This data use for enhancement website interaction. While personalized recommendations design particularly for users based on predictable behavior, website optimization need prioritize influential pages.