• ✨ Statistics graduate & IT Cloud Ops Analyst based in Singapore.
• 🔭 Interested in data, cloud, analytics, machine learning, and related fields.
• 🌱 Growing skills in AWS, GCP, Python, SQL, and machine learning.
• ⚡ Experienced in data analytics, building data pipelines, automating processes, and creating dashboards.
• 📚 Published research in statistical quality control (acceptance sampling) and COVID-19 survival regression analysis.
• 📫 How to reach me:

Here are selected projects completed using Python, Power BI, Tableau, SQL, Looker Studio, and Java:

    Note: The dates indicate the month and year when each project was completed.

• Parcel Delivery Time Prediction (Regression Modeling)   |   Oct 2025   |   Show project
  • Tools: Jupyter Notebook, Python (Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, XGBoost, TensorFlow, PyTorch, Joblib)
  • Description: Built regression models to predict parcel delivery times using historical Amazon order data.
  • Techniques: EDA, feature engineering (date-time transformations, distance calculations), outlier detection, multicollinearity check, mixed scaling (StandardScaler + RobustScaler), feature encoding, and model comparison (Random Forest, XGBoost, TensorFlow DNN, PyTorch DNN).
  • Result: Selected TensorFlow DNN as the final model due to its stability and generalization, achieving competitive MAE, RMSE, and R². Deployed a pipeline with preprocessing and model serialization for inference on new parcel orders.

• SQL Driven Business Analytics Framework   |   Aug 2025   |   Show project
  • Tools: SQL Server, Power BI (DAX)
  • Designed queries for customer, sales, and profitability insights.
  • Built dashboards with KPIs (revenue, profit margin, repeat purchase rate), maps, bar charts, and trend visualizations.
  • Created calculated metrics in Power BI using DAX measures (e.g., Revenue per Order, Profit per Order, Margin per Order, Repeat Purchase Rate).

• Identification of Disaster-Related Tweets (NLP Classification)   |   May 2023   |   Show project
  • Tools: Jupyter Notebook, Python (Pandas, NumPy, Seaborn, Matplotlib, SciPy, Plotly, NLTK, re, collection, wordcloud, TensorFlow, Scikit-learn)
  • Description: Developed an NLP classification model to predict disaster-related tweets.
  • Techniques: EDA, Text Preprocessing, Classification Model Comparison (Linear SVC, Multinomial NB, Neural Network).
  • Result: Achieved AUC 0.86 with Linear SVC, showing strong separation between disaster and non-disaster tweets

• Churn Prediction (IBM Telco Dataset)   |   Apr 2023   |   Show project
  • Tools: Jupyter Notebook, Python (Pandas, NumPy, Seaborn, Matplotlib, Plotly, H3, Folium, TensorFlow, imblearn, Scikit-learn, XGBoost).
  • Description: Built ML models to predict customer churn.
  • Techniques: EDA, Visualization, Classification Model Comparison (Random Forest, Logistic Regression, AdaBoost, XGBoost).
  • Result: Achieved AUC 0.86 with XGBoost.

• Web Scraping Booking.com   |   Apr 2023   |   Show project
  • Tools: Python (Pandas, Requests, BeautifulSoup, RegEx)
  • Description: Scraped hotel data (name, rating, reviews, distance from city center, prices) and processed structured datasets for further analytics.

• Titanic Survival Prediction (Kaggle Competition)   |   Mar 2023   |   Show project
  • Tools: Jupyter Notebook, Python (Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, TensorFlow).
  • Description: Developed ML models to predict survival outcomes.
  • Techniques: EDA, Feature Engineering, Visualization, Classification Model Comparison (Random Forest, Logistic Regression, Complement Naive Bayes).
  • Result: Achieved stratified k-fold CV score of 0.85 with Random Forest.

• Feature Engineering - Convert UTC to Local time   |   Mar 2023   |   Show project
  • Tools: Python (Pandas, DateTime, Dateutil, pytz)
  • Converted UTC time to Malaysia Standard Time for analytics workflows.

• Worldwide Movie Series Visualization   |   Jan 2023   |   Show project
  • Tools: Python (Pandas, NumPy, Seaborn, Matplotlib, wordcloud)
  • Created visualizations highlighting patterns and trends in movie series data.
  • Techniques: EDA, Feature Engineering, Visualization.

• Online Payment Fraud Detection   |   Dec 2022   |   Show project
  • Tools: Jupyter Notebook, Python (Pandas, NumPy, Seaborn, Matplotlib, Tabulate, Scikit-learn)
  • Description: Trained ML models to classify fraudulent vs. non-fraudulent transactions.
  • Techniques: EDA, Visualization, Classification Model Comparison (Random Forest, Logistic Regression).
  • Result: Achieved stratified k-fold CV F1 score of 0.985 using a Random Forest model.

• Cookies Sales Dashboard   |   May 2023   |   Show project
  • Tools: Power BI (Power Query, DAX).
  • Description: Built a dashboard to analyze sales, cost, profit, lead time, and customer trends.

• Flight Ticket Sales Analysis Dashboard   |   Jan 2023   |   Show project
  • Tools: PostgreSQL, Tableau
  • Description: Queried airline ticket data and built dashboards for sales, booking periods, and fare conditions.

• KPMG Data Analytics Consulting Virtual Internship   |   Nov 2022   |   Show project
  • Tools: Python (Jupyter Notebook), Tableau.
  • Conducted data quality assessment and insights analysis.
  • Built Tableau dashboards for customer segmentation and insights presentation.

• Non-parametric Test for Patient Health Status   |   Mar 2022   |   Show project
  • Tools: SAS Studio
  • Description: Applied Shapiro-Wilk, Wilcoxon, Kolmogorov-Smirnov, Kruskal-Wallis, and Spearman’s correlation to patient health data.

• E-commerce Dashboard  |   Jul 2021
  • Tools: Looker Studio (Google Data Studio)
  • Description: Built dashboards displaying sessions, transactions, revenue, checkout behavior, AOV, and conversion rate.

• Java Application -Simple Student Information System   | Nov 2019 |   Show project
  • Tools: Java (NetBeans)
  • Built a Java application to represent a simple student information system.