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๐Ÿ–ฅ๏ธ
Crunching the data
๐Ÿ–ฅ๏ธ
Crunching the data

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mjoshua97241/README.md

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๐Ÿ‘จโ€๐Ÿ’ป About Me

Iโ€™m a Data Scientist and Machine Learning Engineer passionate about merging AI, analytics, and user interfaces to create impactful end-to-end products.
From building scalable fraud detection systems to NLP-powered insight engines, I love solving problems where data meets design.

  • ๐Ÿ”ญ Current: Lead Data Scientist @ AR Data Technologies โ€” building IoT & BIM ML pipelines.
  • ๐Ÿง  Previously: Eskwelabs Fellow โ€” socio-economic prediction models (92.56% accuracy).
  • ๐ŸŒฑ Learning: Generative AI & agentic systems for automated insight extraction.
  • ๐Ÿ’ฌ Ask me about: ML pipelines, NLP, Marimo dashboards.
  • โšก Fun fact: I used to design buildings as an architect โ€” now I design data systems!

๐Ÿง  Tech Stack

Core Skills:

  • ML & AI: XGBoost, TensorFlow, PyTorch, Deep Learning, NLP, LLM Concepts
  • Data: Pandas, NumPy, SQL, Power BI, Seaborn, Matplotlib
  • Web: React, Tailwind CSS, Flask, Streamlit, Cytoscape.js
  • Other: Docker (learning), AWS MLOps (in progress)

๐Ÿš€ Featured Projects

๐Ÿ•ต๏ธโ€โ™‚๏ธ Fraud Detection & Network Mapping (94% Precision)

End-to-end scalable ML pipeline reducing manual fraud review by 4ร—.
Built with XGBoost, Cytoscape.js, and React, featuring 20+ node fraud cluster visualization.
๐Ÿ”— Live Demo

NLP pipeline analyzing AI perception in Philippine media (2020โ€“2025).
Used spaCy, Selenium, and BeautifulSoup to extract sentiment and trends.

Deep Learning model (TensorFlow) predicting user repurchase behavior with 80% accuracy.

Achieved 97.5% accuracy using a two-hidden-layer NN โ€” core deep learning fundamentals.


๐Ÿ–ผ๏ธ Project Gallery


๐Ÿ“ˆ GitHub Analytics


๐Ÿงฉ Current Focus

  • ๐ŸŒ Building AI-driven dashboards with Streamlit and React
  • ๐Ÿงฎ Experimenting with Agentic AI for automated analytics pipelines
  • ๐Ÿ“Š Developing visual storytelling with Power BI + Python

๐Ÿ—๏ธ Experience Snapshot

AR Data Technologies โ€” Lead Data Scientist (2025โ€“Present)
โ†’ Designed data pipeline for IoT & geospatial ML systems.
โ†’ Architected early-stage MLOps dashboard and rule-based prototype.

Eskwelabs โ€” Data Science Fellow (2025)
โ†’ Built Gradient Boosting model (92.56% accuracy) & skill-network analysis using centrality metrics.

VAA Philippines โ€” Amazon PPC Specialist (2023โ€“2025)
โ†’ Automated 40+ performance reports, boosting ad ROI by 20โ€“30%.


๐Ÿ”— Connect With Me

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  1. bank-fraud-detection-project bank-fraud-detection-project Public

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  2. Data-Science-Journey Data-Science-Journey Public

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  3. nlp-ai-perception-ph nlp-ai-perception-ph Public

  4. audiobook-customer-repurchase-prediction audiobook-customer-repurchase-prediction Public

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