Data Science / Engineer Project
This is few of my best Protofolio
Trans Trust(Traffic Congestion & Delay Prediction)
Key Contributions & Technical Implementations:
Engineered an intelligent transportation platform combining Machine Learning and Computer Vision to predict transit delays and monitor real-time traffic congestion.
Deployed a fine-tuned YOLOv8 object detection model to analyze real-time road footage, translating visual traffic data into actionable mobility insights.
Tech/Tools: Python, YOLOv8, Computer Vision, Machine Learning, HuggingFace.
My Role: Data Science
Problem Statement: Public transport delays in Jakarta cost billions. How can we predict transit delays using real-time traffic footage?
The Solution: An intelligent platform that uses Computer Vision to detect traffic density and predict delays.
Used Toyota Corolla Pricing (EDA & Business Insights)
Key Contributions & Technical Implementations:
Airflow Orchestration: Explain how you wrote DAGs in Python to automate the workflow.
The ETL Flow: Explain the journey: Raw data extracted from PostgreSQL →→ Transformed (cleaned) in Python →→ Loaded into Elasticsearch.
Data Validation: This is your secret weapon. Explain how you used Great Expectations to ensure data integrity (e.g., ensuring no negative prices or impossible car years).
My Role: Data Engineer
Problem Statement: How do we automate the extraction, cleaning, and loading of used car pricing data so business analysts can trust the data?
Uber Drive Cancel Booking Prediction
Key Contributions & Technical Implementations:
Feature Engineering: Briefly explain how you handled the Kaggle dataset (handling missing values, encoding categorical data).
XGBoost Model: Why did you choose XGBoost? What were your evaluation metrics? (Mention F1-Score, Precision, or Recall).
Containerization (Docker): Explain how you created the Dockerfile and requirements.txt to ensure the app runs identically in any environment.
Interactive UI: Explain the Streamlit architecture (streamlit_app.py, eda.py, predict.py).
My Role: Data Science
Problem Statement: Ride cancellations cause revenue loss. How can we predict if a user will cancel a ride before it happens?
BMW Car Sales Classification Dataset Analysis
Key Contributions & Technical Implementations:
The SMART Framework: Literally copy your SMART breakdown from your GitHub README to the website. It shows you understand business goals.
Answering the 5W1H: Briefly list the most interesting insights you found (e.g., "Which region has the highest sales?", "Does engine size affect price?").
Inferential Statistics: Mention that you didn't just look at charts; you used statistical testing to prove relationships between variables.
My Role: Data Analyst
Problem Statement: BMW wants to increase car sales volume by 5% over 2 years. Which car specs (engine, fuel, color) should they focus on?
Working in Progress
Layanan Kami
Solusi data dan pengembangan game yang dirancang khusus untuk kebutuhan Anda.
Analisis Data
Mengolah data dengan Python, SQL, dan visualisasi untuk wawasan mendalam.
Pengembangan Game
Membangun game dengan Unity, multiplayer, dan optimasi performa tinggi.
Proyek Data
Kumpulan analisis dan visualisasi data menarik.
Analisis Penjualan
Menggali tren penjualan menggunakan Python.
Visualisasi Data
Membuat grafik interaktif dengan Plotly.
Optimasi SQL
Meningkatkan performa query database.
Prediksi Data
Model prediksi menggunakan machine learning.
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Pengalaman Profesional
Jyotis Sugata telah mengembangkan berbagai proyek game dengan fokus pada Unity, multiplayer, dan optimasi performa, serta melakukan analisis data menggunakan Python dan SQL.
Data
Game Development
Membangun game multiplayer dengan performa tinggi.
Data Science
Experience. With our intuitive design and user-friendly interface, your website will captivate visitors. 2