Category: Geographic Information System (GIS)

This page is meant to store work samples to use as cheat sheets. Thus, the following collection of notebooks are simple and can be modified following required use cases.

Overall, the work samples cover my skill sets, including:

• delivering meaningful data-driven insights to support business goals,
• automating data processing (with python),
• data analysis (tabular, time series, text/NLP, and image),
• descriptive and inferential statistical analysis,
• GIS or spatial data analysis
• data visualization and dashboard development,
• Machine Learning modeling (regression, classification, clustering, dimensionality reduction, time series forecasting, recommender engine)
• Deep Learning or Artificial Intelligence (regression and classification with MLP, image classification with CNN, time series forecasting with LSTM, text classification with LSTM)
• web application development,
• developing APIs,
• Large Language Model (LLM),
• Diffusion (Image Generation) etc.

Highlights:

Task Group	Tasks	Description	Notebook/Repo
Large Language Model (LLM)	Retrieval-Augmented Generation (RAG)	Develop RAG to enhance LLMs with custom documents. Streamlit chatbot as the UI	article, repository
Deep Learning	Image classification with CNN, Multi-label classification	Developed image classification model using CNN to recognize buildings, forest, glacier, mountain, sea, and street images.	CNN
Deep Learning	Time series forecasting with vanilla LSTM, stacked LSTM, bidirectional LSTM, CNN LSTM, and Conv LSTM	Forecast carbon monoxide emission using LSTM and did time series analysis.	Time series
Deep Learning	Text Classification with Dense, LSTM, Bi-LSTM, GRU, CNN, CNN + GRU	Developed text classification model to distinguish tweets into 4 emotions: joy, sadness, anger, and fear.	Text classification
Supervised Learning	Supervised Learning for Remote Sensing	Predicted the spatial distribution of land cover using Remote Sensing/satellite data. Published the result on a web app.	ML + Remote sensing, web app
NLP	NLP and Sentiment Analysis	Performed NLP analysis and text regression for sentiment analysis.	article, part 1, part 2

Others:

Task Group	Tasks	Description	Notebook/Repo
Supervised Learning	Regression	Predicted house prices with various regression algorithms.	Regression
Supervised Learning	Binary classification	Predicted survival rate in titanic using various classification algorithms.	Binary Classification
Supervised Learning	Binary classification (with probability)	Predicted high traffic probability using the metrics of AUC, accuracy, and F1-score.	Binary Classification
Supervised Learning	Multi-class classification	Predicted household poverty as a multi-class classification problem.	Multi-class Classification
Supervised Learning	Imbalanced classification	Predicted whether an employee was a best performer as an imbalanced classification task.	Imbalanced
Supervised Learning	Bayesian Optimization: bayes_opt or fmin	Comparing the libraries bayes_opt and fmin to perform Bayesian optimization for hyperparameter-tuning.	Bayesian Optimization
Supervised Learning	Supervised Learning for Remote Sensing	Predicted the spatial distribution of land cover using Remote Sensing/satellite data. Published the result on a web app.	ML + Remote sensing, web app
AutoML	AutoML for Regression	Predicted house prices with various autoML regression algorithms.	Part 1, Part 2
AutoML	AutoML for Classification	Predicted household poverty classes using autoML classification algorithms.	Part 1, Part 2
Unsupervised Learning	Clustering	Clustered customer segmentation using k-means and hierarchical clustering.	k-means, hierarchical clustering
Unsupervised Learning	Geo-spatial clustering and point pattern analysis	Spatial pattern analysis (point/polygon pattern analysis, Spatially Constrained Hierarchical Clustering, etc. ) of e-commerce customers in Brazil.	Geo-spatial clustering
Unsupervised Learning	Dimensionality reduction: PCA with Sagemaker (upcoming)	Performed PCA on environmental variables dataset.	PCA
Unsupervised Learning	Anomaly detection: Random Cut Forest with Sagemaker	Performed anomaly detection on daily climate dataset and deployed the model using sagemaker.	Random Cut Forest
Time series forecasting	Time series forecasting with SARIMAX	Forecast the cash of ATMs across the time.	“not yet published”
Deep Learning	Image classification with CNN, Multi-label classification	Developed image classification model using CNN to recognize buildings, forest, glacier, mountain, sea, and street images.	CNN
Deep Learning	Time series forecasting with LSTM	Forecast carbon monoxide emission using LSTM and did time series analysis.	Time series
Deep Learning	Text Classification with Dense, LSTM, Bi-LSTM, GRU, CNN, CNN + GRU	Developed text classification model to distinguish tweets into 4 emotions: joy, sadness, anger, and fear.	Text classification
NLP	NLP and Sentiment Analysis	Performed NLP analysis and text regression for sentiment analysis.	article, part 1, part 2
Inferential Statistics	Inferential Statistics, hypothesis testing, etc.		“not yet published”
Dashboard	Shiny Dashboard	Visualized daily covid cases in dashboard.	Shiny Dashboard
Dashboard	Tableau Dashboard	Visualized spatiotemporal analysis of house prices	“not yet published” (upcoming)
Web application	Streamlit	Streamlit as the chatbot interface for an RAG or LLM application	https://github.com/rendy-k/LLM-RAG
API	FAST API		“not yet published”
Sagemaker	Sagemaker: classification	Developed and deployed loan default probability classification using AWS sagemaker.	classification
Sagemaker	Sagemaker: invoke model	Developed the API to invoked deployed Machine Learning model.	invoke model
Sagemaker	Multi-model deployment with Sagemaker	Deployed multi-model on AWS instance.	multi-model deployment
Sagemaker	Recommender system	Built and deployed a recommender system to recommend anime titles using Factorization Machine of AWS.	recommender system
Sagemaker	Time series forecasting	Built and deployed DeepAR to forecast the time series of New Delhi daily weather.	Deep AR
Large Language Model (LLM)		Develop RAG to enhance LLMs with custom documents. Streamlit chatbot as the UI	article, repository

In the “Notebook/Repo” column, the URLs will direct to where the notebooks or repositories are stored. Some of them do not have the URLs, but “not yet published”. This means that the notebooks are available in local computer for professional work. They are not yet modified and published.