Data Cleaning & Preprocessing

NLP / Projects

Sentiment Analysis with SetFit on SST‑5

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In this project, I leveraged SetFit, Hugging Face’s prompt‑free few‑shot classification framework, to tackle the challenging SST‑5 task—fine-grained sentiment analysis across five classes (very negative to very positive) What makes SetFit so compelling is its efficiency and simplicity: This project highlights how evolving NLP techniques—from static embeddings to contextual transformers—impact performance and interpretability. Fine-tuned transformer

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Projects / Regression

Applying supervised learning to predict student dropout

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Student retention is critical for educational institutions, impacting financial sustainability andacademic success. High dropout rates can lead to revenue losses and reputational damage.Study Group, a global education provider, aims to enhance student success by identifying atrisk students early and implementing proactive interventions. This study applies supervisedmachine learning techniques to predict dropout risks, enabling Study Group

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Projects / Unsupervised Learning

Customer segmentation with clustering

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Customer segmentation enables a business to group customers based on demographics (e.g., age, gender, education, occupation, marital status, and family size), geographics (e.g. country, time zone, language, and location), psychographics (e.g. lifestyle, values, personality, and attitudes), behaviour (e.g. purchase history, brand loyalty, and response to marketing activities), technographics (e.g. device type, browser type, and original source),

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Projects / Unsupervised Learning

Ship Engine Anomaly Detection Model

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A poorly maintained ship engine in the supply chain industry can lead to inefficiencies, increased fuel consumption, higher risks of malfunctions, and potential safety hazards. Issues with engines could lead to engine malfunctions, potential safety hazards, and downtime causing delayed deliveries, resulting in the breakdown of a ship’s overall functionality, consequently impacting the business, such

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Projects / Regression

Credit Risk Analysis and Model Prediction for Personal Loan Applications

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In this project, I performed a comprehensive analysis of a loan application dataset to create a model predicting the probability of loan default. The dataset includes applicant information such as age, income, employment background, and loan-specific details like loan amount, interest rate, and purpose. The goal was to develop a model that would enable financial

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Projects / Regression

Vehicle Price Prediction Portfolio

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In this project, The aim is to build a predictive model to estimate used vehicle prices based on various attributes such as brand, model, mileage, engine type, transmission, etc. I am utilizing a dataset containing vehicle information that is readily available on Kaggle. This dataset comprises 188,532 data points, each representing a unique vehicle listing,

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