As businesses increasingly rely on customer feedback to drive their products and services, thedemand for effective customer service solutions has grown. This research paper explores thepotential of Natural Language Processing (NLP) based artificial intelligence (AI) as a solutionfor addressing customer complaints and improving customer service. Through a review ofexisting literature and case studies, this paper examines the effectiveness of NLP-based AI inunderstanding and responding to customer complaints, as well as the potential benefits of usingAI to substitute traditional customer service departments. The paper concludes by discussing theimplications of these findings for businesses looking to improve their customer service offerings,and the potential impact of NLP-based AI on the future of customer service. Overall, thisresearch paper contributes to the ongoing conversation about the role of AI in customer service,and provides valuable insights for businesses seeking to improve their customer feedbackprocesses.

In this project, I focus on employing convolutional neural network techniques for bird image classification and recognition. By leveraging the power of CNNs, my goal is to automatically identify and categorize different bird species based on their visual characteristics. Through careful curation of a comprehensive bird image dataset and utilizing advanced optimization algorithms, transfer learning, and model evaluation, I aim to develop an accurate and efficient model for bird species classification. The outcomes of this project have the potential to support ecological studies, birdwatching activities, and conservation efforts by providing valuable insights and tools for monitoring and protecting avian populations effectively.

This is part of the work at student data scientist role @ WW
This UI is designed to detect and evaluate token classification performance of my re-trained open source BERT language model. By using the UI, the model's performance on the randomly-generated pseudo dataset will be evaluated.

Utilizing a comprehensive and diverse collection of books as my dataset to conduct collaborative filtering, a technique that aims to analyze the preferences and behaviors of users to generate personalized recommendations. With the ultimate goal of delivering an unparalleled book recommendation experience, I am working diligently to develop a sophisticated and advanced collaborative filtering model that takes into account various factors such as user ratings, book genres, author preferences, and publication dates. By combining state-of-the-art machine learning algorithms with cutting-edge data analysis techniques, my aspiration is to create a recommendation system that is both accurate and intuitive, capable of catering to the unique tastes and interests of each individual user

Analyzing the 2008 mortgage crisis, we explore the factors behind default behaviors through data visualization and modeling. Our findings suggest that mortgage issuers should use different models with specific information to measure credit risk, with GDP, FICO score, and Loan to Value ratio playing significant roles in mortgage payment behaviors. As such, financial analysts should closely examine changes in these variables. Different models yield varying variable importance, indicating the need for multiple forecasting methods.

This project aims to build a prediction model using random forest for a small commercial platform. The model will be trained on user behavior data to predict whether a user is likely to exit the platform. An attributional analysis will be conducted on the features to understand which factors are driving the exit behavior. Additionally, we will use a combination of Weibull distribution and exponential modeling to estimate user churn rates. The results of this study will provide valuable insights into user behavior and help the platform to optimize its strategies for retaining users.

The aim of this project was to develop a machine learning model to detect fake job postings. The dataset consisted of over 15,000 job descriptions and requirements, which were preprocessed using stemming and tokenization.
CountVectorizer was used to vectorize the text data, and a Random Forest model was trained to predict fraudulent job postings, achieving an accuracy of 89%.To further analyze the job postings, topic modeling with Latent Dirichlet Allocation was applied, resulting in the classification of the texts into 5 categories based on job functions. Exploratory data analysis was conducted to identify the areas, job levels, and specific words in job descriptions that were highly correlated with fraudulence in each subsection.
Additionally, Locality Sensitive Hashing and KNN were designed to propose alternative predictors based on similarities to historical fake postings. Overall, the project provides insights into the identification of fraudulent job postings and highlights the importance of text mining and machine learning in this process.