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Master in Data Science and Innovation Graduate from the University of Technology Sydney.
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University of Technology Sydney
Data Scientist
June 18, 2026 – Present
Airbnb-Data-Analysis
July 6, 2024 – July 6, 2024
Analysed Airbnb data by building ELT pipelines using Airflow.
View ProjectYouTube_Trend_Analysis
March 25, 2024 – August 8, 2024
Analyzed YouTube trending data across multiple countries using SQL, Snowflake and Azure.
View ProjectLoan-Defaulter-Prediction-Using-Machine-Learning
January 10, 2024 – January 19, 2024
Predictive Analytics in Lending: Machine learning approach to assess loan repayment risks. This repository investigates multiple classifiers against a dataset with socio-economic and financial variables to predict the likelihood of loan defaults.
View ProjectAnalysis-Early-Learning-NSW-ACT
January 10, 2024 – January 10, 2024
Analysis of Early Childhood Education in NSW & ACT to identify underserved areas. Utilising ABS SEIFA, AEDC, ACECQA data, we aim to guide Uniting Early Learning's expansion for equitable access.
View ProjectHotel_Review_Generator
January 9, 2024 – January 10, 2024
A Streamlit based application that uses OpenAI's GPT-3 API to summarise hotel reviews, and expand short review points into concise and meaningful review sentences.
View ProjectTaxi-Fare-Prediction-NYC-using-DataBricks
November 26, 2023 – August 8, 2024
Using Machine Learning to Predict New York City Taxi Fares.
View ProjectFlight_Fare_App
November 8, 2023 – February 20, 2024
A data product developed in Streamlit using Neural Networks and Machine Learning, that will help users in the USA to better estimate their local travel airfare.
View ProjectFlight_Fare_Prediction
October 15, 2023 – November 10, 2023
Models developed using Neural Networks and Machine Learning, that will help to develop an streamlit app that users in the USA to better estimate their local travel airfare.
View ProjectSales_Dynamite
October 11, 2023 – January 24, 2024
A Machine Learning Approach for Retail Sales Forecast and Prediction
View ProjectCultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in applying data science to real-world problems, including social impact (early learning analysis) and business applications (sales forecasting, Airbnb analysis). The projects are diverse in scope and technology, suggesting adaptability and a proactive learning attitude. However, the lack of professional experience beyond a current role starting in 2026 (which seems like a future date or typo) makes it difficult to fully assess cultural fit in a professional setting.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit beyond project descriptions. The candidate's project descriptions indicate an ability to work on diverse problems and apply various data science techniques.