]]> Today with applications being used in distributed environments on a widespread basis, awareness of them is most important in ensuring smooth and effective development of information systems, particularly in those fields where a lot of information has to be processed, such as e-business, banks etc.]]> This proposed study investigates the potential of machine learning methodologies in facilitating early cancer risk assessment by analyzing complex medical datasets. The primary objective is to assess whether machine learning models can reliably identify patients at heightened risk before the disease becomes clinically evident. Through this approach, the study aims to contribute to the development of predictive systems that can trigger early interventions and encourage proactive health monitoring.
The research seeks to answer the core question: “Can machine learning models effectively assess the risk of early-stage cancer using molecular-level data, such as gene expression profiles, prior to the onset of clinical symptoms?”
Sub-questions to be explored include the accuracy of early-stage cancer detection using machine learning, the types of data that most influence prediction performance, and the feasibility of using such models to prompt timely medical evaluations in the absence of traditional diagnostic markers.
The findings are expected to support advancements in personalized medicine by laying the groundwork for tools that assist in identifying high-risk individuals, potentially transforming the current approach to cancer screening and prevention.
]]> The final ensemble achieved an overall accuracy of 76.8% and, critically, a recall of 73% for the churn class, demonstrating strong reliability in identifying at-risk individuals. A key deliverable is an interactive user interface allowing stakeholders to leverage model predictions, visualize churn trends, and interpret the key factors driving customer behavior. This study culminates in a predictive tool that provides actionable insights, enabling businesses to transition from a reactive to a proactive approach in managing customer relationships. By identifying at-risk customers, companies can deploy targeted interventions to enhance loyalty, reduce churn, and secure long-term profitability, creating a practical bridge between advanced analytics and strategic decision-making
]]> The analyzer has gone through several updates: CRAv2 set the foundation with Random Forest classifiers, CRAv3 took a semantic-first approach, and CRAv4 introduced a mixed strategy that fuses rules, semantic pattern analysis, and weighted confidence scoring. There was also an experimental Smart Repository Classifier (SRC) that tried to combine rule-based and machine-learning techniques, plus a side project that looked into detecting design patterns using AST embeddings and Code Property Graphs.
The system was trained on 122 repositories and tested on 47 others across seven different categories. The results were impressive: CRAv3 hit an overall accuracy of 26.4%, while CRAv4 shot up to 70%, with big improvements in web applications (+133%) and CLI tools (+45%). Even though Random Forest tests only maxed out at about 35% in some categories, CRAv4's hybrid method turned out to be both more accurate and easier to understand.
Some key hurdles included the lack of comprehensive datasets, issues with ASTMiner's dependencies, and the author's initial unfamiliarity with machine learning. Tackling these challenges taught valuable lessons in building datasets, feature engineering, and evaluating model

In summary, this project highlights the benefits of blending semantic, structural, and rule-based strategies to gauge developer skills. Looking ahead, plans involve expanding datasets, diving into deep learning for design pattern detection, and enhancing the analyzer into a more general code intelligence tool.
]]> ]]> ]]> ]]> ]]> This project aims to predict the statistical performance of college basketball players upon entering the NBA by integrating structured data (rookie-year statistics) with unstructured data (text-based scouting reports). The proposed system applies NLP techniques to extract features from scouting narratives, focusing on player strengths, weaknesses, and comparisons. These textual features are combined with quantitative performance metrics to develop predictive models for key statistical categories such as points, assists, rebounds, blocks, and steals.
Clustering methods are further applied to group players with similar scouting profiles. Instead of labeling clusters as fixed archetypes, the approach emphasizes identifying the most comparable NBA players for each prospect. This similarity-based framework provides interpretability for scouting evaluations and a practical foundation for projecting performance. The predictive component then leverages historical outcomes of comparable players to generate forecasts for new prospects.


The results demonstrate that clustering can organize players into meaningful groups based on textual analysis, while the predictive framework produces reasonable estimates of rookie-year performance. Together, these findings highlight the potential of integrating NLP and ML into scouting workflows to complement traditional evaluation methods, reduce bias, and enhance the accuracy of performance projections.
]]> The research focuses on evaluating various classical machine learning algorithms for sentiment classification in financial texts. A publicly available dataset combining FIQA and Financial PhraseBank sources was used, containing over 5,800 labeled financial sentences. Data preprocessing steps included cleaning, tokenization, stopword removal, and lemmatization. Exploratory data analysis was conducted to understand sentiment distribution, text length patterns, and word frequencies.
Sentiment labels were encoded numerically to serve as target variables in classification models. A range of traditional machine learning algorithms was implemented and assessed to explore their suitability for sentiment analysis in the financial domain. Evaluation metrics including accuracy, precision, recall, and F1-score were used to assess model performance.
Preliminary results indicate that traditional machine learning models can effectively classify sentiment in financial texts, especially when supported by proper text preprocessing. Furthermore, integrating sentiment analysis with financial data shows potential for improving market forecasting accuracy.


This work contributes to the growing field of financial data science by demonstrating the effectiveness of NLP-driven sentiment analysis and offering a framework for building predictive systems that combine textual and numerical financial indicators.
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