| User behavior analysis is a crucial method employed by businesses to enhance user quality and achieve targeted marketing.The key lies in accurately predicting user behavior.However,user information is complex and diverse,often containing a significant amount of irrelevant or duplicated data,which poses challenges for traditional user behavior prediction models in achieving high accuracy.Deep learning,a new research field in machine learning,offers promising solutions,and one such algorithm within deep learning,known as deep forest,has found wide applications in user behavior prediction.The deep forest algorithm consists of multiple hierarchical layers,with each layer’s units transforming the input data into informative representations for subsequent prediction tasks.To address the challenge of low prediction accuracy in user behavior,this thesis adopts a machine learning approach and investigates the use of the deep forest algorithm for user behavior prediction.Through empirical comparisons on existing datasets,the deep forest algorithm demonstrates superior accuracy in user behavior prediction.The thesis begins by examining and interpreting user information data.Visual analysis is then conducted from two perspectives: user information and user behavior.User information analysis includes the distribution of users across cities and changes in user increments,while user behavior analysis focuses on login days,course enrollment,and course conversion rates.Furthermore,user segmentation is performed based on the RFM model,and recommendations are provided for managing and communicating with users in different segments.Next,the thesis preprocesses the user data,which involves removing or imputing missing values,eliminating duplicates and outliers.To address imbalanced data,the SMOTE resampling technique is employed to create a new dataset with an equal number of positive and negative samples,followed by feature selection.The thesis adopts the XGBoost-PCA fusion method for feature selection,where the XGBoost algorithm is used to rank feature importance,and the PCA method is employed for dimensionality reduction,selecting the most important indicators for subsequent analysis.Finally,logistic regression,decision tree,random forest,deep forest,and an improved weighted deep forest model are employed for empirical analysis.The results indicate that the improved weighted deep forest model outperforms other machine learning models in various evaluation metrics.The thesis innovatively applies the XGBoost-PCA fusion method and the weighted deep forest model,which significantly enhance the accuracy of predictions by incorporating weights in the decision tree predictions.In conclusion,this thesis proposes a deep learning-based user behavior prediction model through visual analysis of user behavior data,user segmentation,and empirical analysis using machine learning algorithms.The research findings demonstrate that the improved deep forest algorithm exhibits high accuracy and reliability in predicting user behavior,offering valuable insights for businesses to enhance user quality and implement targeted marketing strategies.The employed XGBoost-PCA fusion and weighted deep forest algorithms also contribute to the innovation and practicality of machine learning techniques.Overall,the research findings presented in this paper provide important insights for improving the accuracy of user behavior prediction and optimizing marketing strategies for businesses.The adopted XGBoost-PCA fusion and weighted deep forest algorithms also contribute to the innovation and practicality of the field of machine learning. |
