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Rudra Prasad Bhuyan

Rudra Prasad Bhuyan

Aspiring Data Scientist | ML Engineer

Machine Learning Engineer with hands-on experience in data analysis, machine learning, and end-to-end project execution. Strong focus on real-world datasets, feature engineering, model evaluation, and practical problem solving through self-driven learning, internships, and open-source work.

Focused on practical, system thinking solutions — not just models. I solve real problems using data.

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Skills & Expertise
Languages: Python, SQL
Data Analysis Framework: Pandas, Numpy
Visualization: Matplotlib, Seaborn, Plotly, Power BI
ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost
MLOps & Tools: Docker, MLflow, Git, FastAPI
Work Experience

Junior Data Scientist Intern @ SBC Labs

Nov 2025 – Feb 2026
  • Developed a structured data analysis framework using Python to analyze an India-level open-source dataset with 400+ features, identifying high-impact variables and reducing feature redundancy.
  • Optimized data quality through extensive data cleaning and preprocessing in Python, transforming messy raw data into model-ready datasets and improving downstream analytics efficiency.
  • Built a state-level interactive dashboard using Python visualization libraries, enabling focused insights for one state and improving decision-making clarity for stakeholders.
  • Improved team understanding by creating a comprehensive Feature Requirement & Documentation Sheet, reducing dataset confusion and standardizing feature interpretation across the team.
  • Collaborated with technical and non-technical members by delivering structured weekly reports and translating complex data findings into actionable insights, enabling informed project decisions.

Electrical Engineering Intern @ Tata Power

June 2025 – July 2025
  • Gained hands-on exposure to electricity distribution operations, including smart meter functionality, maintenance workflows, and field service processes.
  • Observed and analyzed the end-to-end digital complaint management system, including online application handling, user request processing, and new connection workflows.
  • Understood field-to-system integration where service updates and on-site photographic evidence were recorded and synchronized within Tata Power's internal operations platform.
Open Source

show-file-tree

A small, fast CLI tool to display styled file/folder trees with rich options, colors, icons, and metadata.

PyPI Docs GitHub

find-my-joint

A utility to find potential join keys (matching columns) across multiple pandas DataFrames.

PyPI Docs GitHub
Featured Projects

Vehicle Insurance Risk Prediction

Problem

[Ongoing] Insurance companies need to estimate the risk associated with insuring a vehicle. Accurate risk prediction helps companies: Reduce financial loss, Price policies correctly, Detect high-risk customers.

Tools

Python, Flask, NoSQL, AWS, CI/CD, Docker

  • Built an end-to-end MLOps pipeline to predict vehicle insurance risk using automated data ingestion, transformation, training, and deployment.
  • Integrated MongoDB, AWS S3, and EC2 with a modular ML pipeline for scalable and production-ready workflows.
  • Implemented CI/CD using GitHub Actions and Docker for seamless model deployment and updates.
  • Developed a Flask-based web app for real-time prediction and user interaction.
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SQL Modern Data Warehouse

Problem

Raw ERP & CRM sales data was scattered in CSV files, inconsistent, and not analytics-ready for business reporting.

Tools

PostgreSQL, SQL (ETL), Star Schema Modeling, EDA, Power BI

  • Built a 3-layer Medallion architecture (Bronze–Silver–Gold) in PostgreSQL, transforming raw CSV data into analytics-ready tables.
  • Developed end-to-end ETL pipelines using SQL, integrating ERP & CRM data into a scalable star schema model.
  • Optimized business reporting by creating analytical views and SQL reports for customer behavior, product performance, and sales trends.
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Yelp Big Data Capstone

Problem

Traditional tools like Pandas struggle with large Yelp JSON datasets due to memory and performance limitations.

Tools

Python, Polars, JSON, Parquet, Jupyter Notebook

  • Built a high-performance data processing pipeline using Polars, handling large Yelp JSON files efficiently without memory bottlenecks.
  • Optimized storage and I/O by converting raw JSON data into Parquet format, improving processing speed and scalability.
  • Developed step-by-step analytical workflows (filtering, groupby, joins, rolling ops) to extract structured insights from large-scale business data.
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Breast Cancer Prediction App

Problem

Need for an interactive, real-time diagnostic tool to predict whether a tumor is benign or malignant using medical imaging features.

Tools

Python, Scikit-learn (Logistic Regression), Streamlit, Pandas, Plotly

  • Built an end-to-end ML pipeline using Logistic Regression (Scikit-learn) for tumor classification based on 30 diagnostic features.
  • Developed a real-time web application in Streamlit, delivering instant predictions with probability confidence scores.
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