Integrating Predictive Analytics with Customer Behavior Data in E-commerce Based on Machine Learning Model
Bibliographic record
Abstract
In modern competitive e-commerce, consumer behavior is vital to understand and predict to raise engagement, reduce churn, and streamline company strategy. Conventional machine learning models do not tend to capture complex and evolving consumer behavior, leading to mediocre prediction performance. The proposed paper presents a hybrid Model, which integrates the Bidirectional Long Short-Term Memory (BiLSTM) networks to learn long-term sequential connections between consumer behavior data and Convolutional Neural Networks (CNN) to extract local features. The mitigation of data quality and imbalance is done through the extensive preparation steps of the methodology, which involve handling missing values, one-hot encoding, min-max normalization, and SMOTE-based class balancing. Several additional models such as the Random Forest, Logistic Regression, Stochastic Gradient Boosting, SVM, and two novel models namely K-Nearest Neighbors (KNN) and CNN-BiLSTM were also tested and reviewed. The CNN-BiLSTM model scored significantly higher to its competitors with 97% accuracy (Acc), 99.8% recall (Rec) and 99.8% F1score, indicating a high ability to learn complex and non-linear patterns; KNN achieved 96% accuracy. The findings confirm the proposed methodology in terms of reliable and effective e-commerce customer turnover prediction
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".