I'm an enthusiastic data scientist with over eight years of experience in data analysis, data visualization, and data storytelling. I enjoy solving challenging problems, harnessing the power of machine learning to derive valuable insights, and effectively communicating complex information.

Category	Skill
Programming
Data Manipulation
Data Visualization
Machine Learning
Big Data
Web Framework
Cloud Computing
Version Control
Code Editors

• Motivation: Simplify the process of finding rental properties in Singapore's expensive real estate market by using machine learning to estimate rental prices.
• Data collection: Scraped 1680 property listings from an online property portal, including information on price, size, address, bedrooms, bathrooms and more.
• Exploratory data analysis: Visualized property locations on an interactive map, generated a word cloud to extract insights from property agent descriptions, and examined descriptive statistics, distributions, and correlations.
• Data preprocessing: Handled missing address data and engineered location-related features using the Google Maps API, extracted property features from agent descriptions and systematically evaluated multiple outlier handling methods.
• Model training: Trained five machine learning models with baseline configurations, selected an XGBoost regression model with optimized hyperparameters, and achieved a test dataset performance with an RMSE of 995, a MAPE of 0.13, and an R² of 0.90.
• Model deployment: Created a web application for serving the XGBoost model using the Flask framework. Containerized this application using Docker and successfully deployed the Docker container on render.com.

• Motivation: Develop a hate speech detector for social media comments.
• Data: Utilized the ETHOS Hate Speech Detection Dataset.
• Models: Trained and evaluated the performance of three deep learning models using TensorFlow and scikit-learn. The fine-tuned BERT model demonstrated superior performance (78.0% accuracy) compared to the SimpleRNN (66.3%) and LSTM (70.7%) models.
• Deployment: Prepared the fine-tuned BERT model for production by integrating it into a web application and an API endpoint using the Flask web framework.

Fine-tuned BERT: Confusion Matrix	Model Deployment

• Developed an AI-assisted cover letter generator that empowers job seekers in crafting personalized and professional cover letters tailored to specific job offers.
• Scraped job postings by employing Python and Beautiful Soup and utilized the ChatGPT API to extract key information, including requirements and tasks, in JSON format.
• By leveraging the ChatGPT API further, cover letter suggestions were generated, aligning the candidate's education, work experience, skills, and motivation with the specific job's requirements and tasks.

{
  "employer": "OpenAI",
  "job title": "Research Scientist",
  "requirements": [
    "Track record of coming up with new ideas or improving 
    upon existing ideas in machine learning",
    "Ability to own and pursue a research agenda",
    "Excitement about OpenAI's approach to research",
    "Nice to have: Interested in and thoughtful about the 
    impacts of AI technology",
    "Nice to have: Past experience in creating high-performance 
    implementations of deep learning algorithms"
  ],
  "tasks": [
    "Develop innovative machine learning techniques",
    "Advance the research agenda of the team",
    "Collaborate with peers across the organization"
  ],
  "contact person": "unknown",
  "address": "San Francisco, California, United States"
}

Advanced SQL: MySQL for Ecommerce & Web Analytics, Udemy, February 2024, 🔗 see certificate
Skills: MySQL · SQL

AWS Certified Cloud Practitioner, AWS, January 2024, 🔗 see certificate
Skills: Amazon Web Services (AWS)

Ultimate AWS Certified Cloud Practitioner CLF-C02, Udemy, January 2024, 🔗 see certificate
Skills: Amazon Web Services (AWS)

Spark and Python for Big Data with PySpark, Udemy, January 2024, 🔗 see certificate
Skills: Spark · PySpark · AWS · Python · Machine Learning · Linear Regression · Logistic Regression · Decision Trees · Random Forest · Gradient Boosting · k-means clustering · Recommender Systems · Natural Language Processing (NLP)

Microsoft Power BI Data Analyst, Udemy, November 2023, 🔗 see certificate
Skills: Power BI

Deep Learning, alfatraining Bildungszentrum GmbH, April 2023
Skills: TensorFlow · NumPy · Natural Language Processing (NLP) · Python · Deep Learning · Recurrent Neural Networks (RNN) · Neural Networks · Scikit-Learn · Reinforcement Learning · Transfer Learning · Convolutional Neural Networks (CNN) · Time Series Analysis

Machine Learning by Stanford University & DeepLearning.AI, Coursera, April 2023, 🔗 see certificate
Skills: Decision Trees · Recommender Systems · Anomaly Detection · Python · Linear Regression · Neural Networks · Logistic Regression · Reinforcement Learning · Principal Component Analysis · k-means clustering

Python for Machine Learning & Data Science Masterclass, Udemy, March 2023, 🔗 see certificate
Skills: Decision Trees · Support Vector Machine (SVM) · Matplotlib · Random Forest · Naive Bayes · NumPy · Seaborn · Hierarchical Clustering · Natural Language Processing (NLP) · Pandas · Python · Linear Regression · Scikit-Learn · Logistic Regression · Principal Component Analysis · Gradient Boosting · DBSCAN · k-means clustering · K-Nearest Neighbors (KNN)

Machine Learning, alfatraining Bildungszentrum GmbH, February 2023
Skills: Decision Trees · Support Vector Machine (SVM) · Matplotlib · Naive Bayes · NumPy · Hierarchical Clustering · Pandas · Python · Linear Regression · Neural Networks · Scikit-Learn · Principal Component Analysis · DBSCAN · k-means clustering · K-Nearest Neighbors (KNN)

The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert, Udemy, December 2022, 🔗 see certificate
Skills: MySQL · SQL

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