🔆 I am passionate Data Analyst / Data Scientist 📈 📉 who has worked in interationnal luxury retail group and equine hospital 🏢.

🔆 I enjoy a lot in helping companies to discover and empower the potential of the data with data science tools and algorithms.

🔆 Self-motivated and eager to learn different technologies and concepts. Recently, I am learning Scala and DBT.

• Sales and traffic forecasting
• Data anomaly detection
• Stock price prediction with algo-trading
• Mobile price range classification
• Recommendation system
• Social network analysis

• Sales performance dashboard
• Network traffic monitoring dashboard
• Drugs and vaccination usage dashboard
• Injuries and rehabilitation dashboard

• Python: Numpy,Pandas,Scipy, multiprocessing
• R: ggplot2, tidyr
• SQL
• Scala
• Java
• C++

• Software: Power BI, Tableau, Snowflake
• Library: Plotly, Seaborn, Matplotlib

• Frameworks: statsmodels, scikit-learn
• Regressions: Linear, Logistic, Lasso, Ridge
• Boosting and Trees: XGBoost, Catboost, Adaboost, Decision Tree, Random Forest
• Clustering: SVM, K-Means, LOF, DBSCAN
• Time Series: Prophet, DeepAR, SARIMAX

• Frameworks: PyTorch & Keras
• Models: CNN, RNN, LSTM, Transformer
• Large Language Model: Hugging Face

• H2O.ai

• DBT

• Git
• Docker
• Airflow

• Platforms: Google Cloud Platform, Amazon Web Services

• Frameworks: Spark, Hadoop
• Python API: PySpark, Dask

• Regression Analysis
• Correlation Analysis
• Statistical Analysis

• Agile Methodology & Kanban
• Scrum & Sprint
• JIRA
• Confluence
• Streamlit
• Flask

• Data Cleaning
• Exploratory Data Analysis
• Feature Engineering
• Feature Extraction

• Prompt Engineering
  • Zero-shot Inference
  • One-shot Inference
  • Few-shot Inference
• Fine-Tuning;
  • Instruction Fine-Tuning
  • LoRA
  • Soft Prompt

• Utilized ensemble learning and deep learning to predict the price range of a used car in the North American market.
• Aimed to provide a data-driven price prediction for buyers and sellers to improve market efficiency. Try it out now!

• Applied Logistic Regression, gradient boosting, random forest, KNN and Naive Bayes to predict mobile phone price ranges.
• Predict the price range of mobile phones based on their functionality and hardware components.
• Dataset from Kaggle and achieve 93.8% of weighted F1 score in baseline model - Logistic Regression.

• Perform predictive analysis and linear & logistic regression to predict the presence of West Nile Virus.
• Perform exploratory data analysis and data cleaning before data modelling

• Applied SVC, XGBoost, Catboost, Prophet and CNN-LSTM to predict stock prices.
• Make trading decisions based on our model prediction and users' risk classification.
• Model Performance outperforms “Buy-and-hold” Strategies.

• Perform network analysis to spot the key opinion leader in the network.
• Applied Random Walk Generator to extract information from local and global networks.
• Applied DeepWalk and Node2Vec for embedding stage
• The AUC-ROC score reached 0.9323.

• Applied the Neural Collaborative Filtering (NCF) model in the recommendation system to predict the user's rating (1-6).
• Used Wide & Deep Learning model for prediction.
• RMSE dropped to 0.99.

• Applied MLP, Flair, CNN and BERT with the Pytorch framework to predict the score of restaurant reviews.
• Utilized various techniques such as tokenization, stopword removal, stemming and word embedding with Word2Vec & GloVe

• Perform data analysis with Tableau on Air Traffic Data
• Analyse the performance of major US Airlines such as Delta Airlines, American Airlines and Southwest Airlines

• Provide data-driven recommendations based on the past 10 years of Kickstarter campaign data.
• Perform visualizations to support the business insights and recommendations.