Capstone Project

• Conducted a survey on modern methods and technologies using NLP to detect ambiguity and inconsistencies in the legal domain, resulting in a paper (not yet published) that highlights the potential of NLP algorithms in resolving legal ambiguity and contradiction.
• Currently developing a large language model using NLP methods to detect ambiguity and contradictions in previous legal text, with the aim of creating a powerful tool for legal professionals to improve the accuracy and efficiency of contract interpretation.

Machine Learning:

Algorithms Used: Linear Regression, SVM, K-Means, SVM-Kernel, Logistic Regression, KNN, Decision   Tree, Naive Bayes, Random Forest, Gradient Descent (Python)

Data Science:

Algorithms Used: Apriori, Decision Tree, Random Forest (Python)

Statistical Analysis:

Analysis Methods: ANOVA, Pearson Correlation, Multiple Linear Regression, Power Sample Size, Inference Proportions, Chi-Squared, T-test, Logistical Regression (R)

Databases in SQL:

Relation Schema, Entity Relationship Diagram/Schema, SQL Databases Created: Hospital, Airlines (Scheduling) (PostgresSQL)