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P. Kanisius Bagaskara

Machine Learning Engineer (Student)

Universitas Pamulang

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Preparing Pit Lane...

KANISIUS

BAGASKARA

Google Student Ambassador
Universitas Pamulang • Tangerang, Indonesia
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Selected Work

Featured Projects

Production-ready ML systems with real-world impact. Each project includes problem analysis, technical implementation, and measurable results.

01

F1 2025 Analytics Dashboard

Real-time telemetry & ML-powered strategy predictions

Live DemoCode
The Problem

F1 fans struggle to understand race strategies. Existing tools lack real-time ML predictions for tire degradation and optimal pit stops.

My Solution
  • →Interactive telemetry visualization using FastF1 API
  • →XGBoost model predicting optimal pit windows (92% accuracy)
  • →Driver performance comparison across 23 race tracks
  • →Weather integration affecting strategy recommendations
Impact
92%
Model Accuracy
<50ms
API Response
50+
Active Users
Challenges Solved
  • •Handled missing telemetry data with interpolation algorithms
  • •Optimized API fetching within rate limits
  • •Built mobile-responsive UI for live race viewing
PythonStreamlitFastF1XGBoostFastAPI
02

IEEE-CIS Fraud Detection

Binary classification for financial transaction security

Code
The Problem

Financial fraud detection requires handling imbalanced datasets and complex transaction patterns.

My Solution
  • →XGBoost classifier with comprehensive feature engineering
  • →Transaction pattern analysis & anomaly detection
  • →Handled class imbalance with SMOTE
  • →ROC-AUC optimization for fraud detection
Impact
0.94
ROC-AUC
590k+
Dataset
400+
Features
Challenges Solved
  • •Extreme class imbalance (3.5% fraud cases)
  • •Feature engineering from anonymized data
  • •Time-based validation to prevent leakage
XGBoostPandasScikit-learnSeaborn
03

AI Data Scraping Pipeline

Automated ML dataset collection & preprocessing

Code
The Problem

ML projects need quality data, but manual collection is time-consuming and inconsistent.

My Solution
  • →Automated web scraping with BeautifulSoup & Selenium
  • →Data validation and cleaning pipeline
  • →Rate limiting and error handling
  • →Scheduled updates with cron jobs
Impact
Learning
Status
Automation
Focus
Pipeline
Goal
Challenges Solved
  • •Handling dynamic JavaScript-rendered content
  • •Proxy rotation to prevent IP blocking
  • •Data validation and deduplication
BeautifulSoupSeleniumPandasRequests
View More on GitHub

Tech
Stack

Python
01

Python

Primary

PyTorch
02

PyTorch

Deep Learning

TensorFlow
03

TensorFlow

ML Production

Keras
04

Keras

Neural Networks

Scikit-Learn
05

Scikit-Learn

ML Algorithms

HuggingFace
06

HuggingFace

NLP & LLM

LangChain
07

LangChain

LLM Apps

Pandas
08

Pandas

Data Manipulation

NumPy
09

NumPy

Numerical

OpenCV
10

OpenCV

Computer Vision

Python
01

Python

Primary

PyTorch
02

PyTorch

Deep Learning

TensorFlow
03

TensorFlow

ML Production

Keras
04

Keras

Neural Networks

Scikit-Learn
05

Scikit-Learn

ML Algorithms

HuggingFace
06

HuggingFace

NLP & LLM

LangChain
07

LangChain

LLM Apps

Pandas
08

Pandas

Data Manipulation

NumPy
09

NumPy

Numerical

OpenCV
10

OpenCV

Computer Vision

Matplotlib
11

Matplotlib

Plotting

Seaborn
12

Seaborn

Statistical Viz

Plotly
13

Plotly

Interactive

Docker
14

Docker

Containers

GitHub Actions
15

GitHub Actions

CI/CD

GitLab CI
16

GitLab CI

DevOps

VS Code
17

VS Code

Editor

Google Colab
18

Google Colab

Cloud

Jupyter
19

Jupyter

Notebooks

Streamlit
20

Streamlit

Data Apps

Matplotlib
11

Matplotlib

Plotting

Seaborn
12

Seaborn

Statistical Viz

Plotly
13

Plotly

Interactive

Docker
14

Docker

Containers

GitHub Actions
15

GitHub Actions

CI/CD

GitLab CI
16

GitLab CI

DevOps

VS Code
17

VS Code

Editor

Google Colab
18

Google Colab

Cloud

Jupyter
19

Jupyter

Notebooks

Streamlit
20

Streamlit

Data Apps

By The Numbers

The journey so far

8+
GitHub Repositories
200+
GSA Top Rank
35+
Tech Stack
4
Major Projects
Track Record

By The Numbers

Quantifiable impact through code, education, and continuous learning.

8+
Repositories
200+
GSA Top Rank
35+
Tech Stack
4
Major Projects

Technical Impact

8+Open Source Projects

ML and data science tools

50+GitHub Stars

Across all repositories

92%Model Accuracy

F1 tire degradation predictor

Education & Teaching

Top 200Google Student Ambassador

From 12,000+ applicants

5+ML Workshops

Conducted for students

100+Students Trained

In ML and data science

Recognition

StanfordML Specialization

3 courses completed

GoogleGemini Certified

Educator & Student

IBMData Science

Professional certificate

My Journey

The Path So Far

From watching Iron Man to becoming a Google Ambassador—every step counts.

2025

Started ML Journey

November 2025 - Inspired by Iron Man's JARVIS, began learning Python and ML fundamentals

First Classification Model

Built Iris dataset classifier using scikit-learn

Joined Data Science Club

Universitas Pamulang - started community learning

2025

IBM Python for Data Science

Completed professional certificate (after November)

Google Student Ambassador

Selected as Top 200 from 12,000+ applicants in Indonesia

First ML Workshop

Conducted workshop for 50+ students

F1 Analytics Dashboard

Launched real-time telemetry analysis tool

Google Gemini Certified

Both Educator and Student certifications

2026

Stanford ML Specialization

Completed 3-course program with honors

Deep Dive into ML

Implemented 8 CS229 algorithms from scratch

Currently Building

F1 tire degradation predictor with weather integration

Now

“The journey of a thousand miles begins with a single step”

— And a lot of Python debugging

Continuous Learning

Certifications & Credentials

Stanford ML Specialization, IBM Data Science, and Google AI certifications with hands-on projects and measurable outcomes.

Featured

Machine Learning Specialization

Stanford University • DeepLearning.AI • January 2026

Course 1Course 2Course 3

Comprehensive ML curriculum covering supervised, unsupervised, and advanced algorithms with mathematical foundations. Built production-ready models with focus on vectorization and optimization techniques used in real-world ML systems.

01

Supervised Machine Learning

  • Linear & Logistic Regression
  • Gradient Descent
  • Regularization
  • Neural Networks
Project:

Housing price prediction with 90% R² score

02

Advanced Learning Algorithms

  • Deep Learning
  • Decision Trees
  • Random Forest
  • XGBoost
Project:

Multi-class classification with F1: 0.94

03

Unsupervised Learning

  • K-Means Clustering
  • PCA
  • Anomaly Detection
  • Recommender Systems
Project:

Movie recommendation system

PythonNumPyScikit-learnTensorFlowMathematical Optimization
IBM

Python for Data Science

IBM • Coursera • August 2025

Data analysis, visualization, and machine learning with Python. Pandas, NumPy, and Scikit-learn fundamentals.

PandasMatplotlibSeabornData AnalysisSQL
Download Certificate
Google

Gemini Certified Educator

Google for Education • September 2025

Advanced training in Google's Gemini AI tools for educational contexts and workshop facilitation.

AI EducationPrompt EngineeringWorkshop Design
Download Certificate
Google

Gemini Certified Student

Google for Education • October 2025

Comprehensive understanding of Gemini AI capabilities for learning and productivity enhancement.

AI LiteracyProductivity ToolsResearch Methods
Download Certificate
About Me

Turning Data Into Racing Insights

Machine Learning Engineer focused on building production-ready ML systems and real-time analytics. Currently developing an F1 tire degradation predictor with weather integration using Python, FastF1 API, and XGBoost.

As a Google Student Ambassador Top 200, I've conducted ML workshops for 100+ students, making complex AI concepts accessible through hands-on learning.

My approach combines rigorous engineering with practical application—whether that's implementing algorithms from scratch (Stanford CS229) or deploying models that process real-time telemetry data.

Top 200 Google Student Ambassador
Stanford ML Specialization
8+ Open Source Projects
100+ Students Trained

Core Expertise

Deep Learning

PyTorch, TensorFlow, Neural Networks, Computer Vision, NLP

MLOps

Docker, CI/CD, Model Deployment, API Development

Data Engineering

ETL Pipelines, Web Scraping, FastF1 API, Feature Engineering

Leadership

Google Student Ambassador, ML Workshops, Public Speaking

Open for Opportunities

ML Engineering internships & freelance projects

Random Facts

How I Got Here

01

The Iron Man Thing

Honestly? I got into Data Science because of Iron Man. Watching JARVIS talk, analyze data in real-time, help Tony make decisions—I thought, "Damn, I wanna build something like that." Started learning Python, fell into ML, and now I'm stuck here (in a good way).

02

From Bedroom to Stage

Used to be just some kid coding alone in my room. Next thing I know, I'm a Google Student Ambassador. I was the guy who got nervous talking to 5 people. Now I can teach workshops with 100+ people. Didn't see that coming, but turns out teaching is fun—it actually makes me understand stuff better.

03

The Stubbornness

I never aim to be the best. I just know that when I have a target, I chase it until I get it. Like F1—it's not about being perfect, it's about improving every single lap. Code broke? Fix it. Algorithm failed? Try again. Just keep moving forward.

I'm not perfect, but when I have a goal,
I DON'T STOP UNTIL I GET IT.

Too stubborn to quit
Always LearningToo Stubborn To QuitF1 > Football
Get In Touch

Let's Build Something

Currently open to opportunities. Usually respond within 24 hours.

Open For

ML Engineering internships
Freelance data science projects
F1 analytics collaboration
Guest speaking at tech events
Start a ConversationDownload CV
Usually respond within 24 hours
Email
maxvy1218@gmail.com
LinkedIn
linkedin.com/in/kanisiusbagas1212
GitHub
github.com/maxvyquincy9393
Twitter
@maxquincy18
WhatsApp
+62 851-8326-8643
Live

Currently

Recent Win

Completed Stanford ML Specialization (3 courses)

Last updated: February 2026

This section auto-updates with my latest activities