Proceedings of the International scientific and practical conference ―Oxford International Science Forum‖ (April 17-19, 2026) / Publisher website: www.naukainfo.com. - Oxford, United Kingdom, 2026. - 367 p.

105 boosting [3, 6]. These approaches can be complemented by explainable AI tools such as feature importance rankings and local explanation methods. Temporal feature engineering, moving window analysis, survival-oriented modeling, boosting methods, and sequence-aware learning are suitable for early-stage outcome prediction [4-6]. For text analytics, NLP is central. Relevant methods include tokenization, lemmatization, TF-IDF representation, sentiment analysis, topic modeling, semantic embedding, and transformer-based large language models (LLMs) [4-5, 12-13]. Clustering algorithms such as k-means, hierarchical clustering, density-based clustering, and mixture models can be applied to segment campaigns based on combinations of financial, textual, and temporal features [2], [6]. For anomaly detection, suitable tools include isolation forest, local outlier factor, single-class classification, change point analysis, and hybrid rule-based scoring systems [6]. In practice, the most appropriate technical environment includes Python-based data engineering and machine learning libraries, NLP toolkits, explainability frameworks, and dashboard tools for visual decision support. Such a pipeline should support data collection, preprocessing, multimodal feature extraction, predictive modeling, interpretation, and visualization [4-6], [12-13]. 3. Results and Discussions Obtained analytical insights from CD . The practical value of CD analysis lies in the possibility of obtaining useful conclusions. Success factors. One major class of conclusions concerns the identification of factors associated with the success of a campaign. Such factors may include the size of the goal, duration, campaign category, contributor activity, social capital, clarity of text, frequency of updates, and intensity of early-stage funding [1-2, 4-6]. Predicting results early. Another important aspect is the ability to predict campaign results early. By combining initial funding dynamics, engagement signals, and campaign content features, analysts can estimate the likelihood of success before the campaign is complete [4-6].