Available for opportunities

Hi, I'm Biplov Singh

I build

Data Science & Machine Learning developer specializing in end-to-end ML pipelines, NLP systems, and data-driven solutions. Transforming raw data into meaningful insights.

View My Work Download Resume

3+ ML Projects

5+ Technologies

∞ Curiosity

biplov_singh.py

class BiplovSingh:
  def __init__(self):
    self.name    = "Biplov Singh"
    self.domain  = "biplovsingh.dev"
    self.focus   = [
      "Machine Learning",
      "Deep Learning / NLP",
      "Data Engineering",
    ]
    self.stack   = {
      "lang": "Python,C,C++",
      "data": "Pandas,Numpy",
      "viz": "Matplotlib,Seaborn",
      "dl":   "PyTorch",
      "db":   "MySQL / MongoDB",
      "os":   "Linux / Ubuntu",
      "other": "Git,Docker",
    }

  def available(self):
    return True  # open to work!

01. about

About Me

The story behind the code

I'm a Data Science student with a passion for turning complex data into intelligent, real-world solutions.

My journey into machine learning started with a curiosity about how computers can learn patterns from data — and it quickly became an obsession. I specialize in building end-to-end machine learning pipelines, from raw data collection and preprocessing through model training, evaluation, and deployment.

I'm equally comfortable wrangling messy datasets, fine-tuning transformer models for NLP, designing efficient SQL schemas, or spinning up experiments in a Linux terminal. I believe great ML engineering is as much about robust infrastructure as it is about model accuracy.

Beyond core data science, I have a growing interest in cybersecurity and penetration testing — understanding how systems can be attacked helps me build more secure and resilient data applications.

Education Data Science (Student)

Location Jamshedpur, Jharkhand

Environment Linux / Ubuntu

Website biplovsingh.dev

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PyTorch

Linux

Python

terminal

biplov@dev:~/projects $ python train.py

Epoch 1/10 — loss: 0.4231 — acc: 0.8740

Epoch 5/10 — loss: 0.1823 — acc: 0.9310

Epoch 10/10 — loss: 0.0944 — acc: 0.9680

✓ Model saved → ./models/best.pt

biplov@dev:~/projects $ █

02. skills

Tech Stack

Tools and technologies I work with

Core Languages

Python

Expert

SQL (MySQL)

Proficient

Bash / Shell

Proficient

ML & Deep Learning

PyTorch

Proficient

Machine Learning

Proficient

Deep Learning

Proficient

NLP / Transformers

Learning

Data & Visualization

Pandas / NumPy

Proficient

Matplotlib / Seaborn

Proficient

Data Visualization

Proficient

Systems & Security

Linux / Ubuntu

Proficient

Cybersecurity

Learning

Penetration Testing

Learning

Expert Proficient Learning

03. projects

Featured Projects

A selection of my most impactful work

01 Featured

Aspect Pulse

Aspect-Based Sentiment Analysis (ABSA) System

A fine-grained sentiment analysis system that goes beyond document-level polarity. Aspect Pulse identifies specific aspects (e.g., battery life, camera, performance) within product reviews and assigns independent sentiment scores to each. Built with transformer-based NLP models and a structured data pipeline.

Fine-grained aspect extraction from unstructured text

Transformer-based sentiment classification per aspect

End-to-end preprocessing, training, and inference pipeline

Evaluation on benchmark ABSA datasets

Python PyTorch Transformers NLP ABSA Hugging Face

Reddit Sentiment Pipeline

Public Opinion Mining & Data Engineering

A scalable data pipeline that ingests Reddit posts via the PRAW API, processes and cleans the raw text, applies sentiment analysis models, and stores structured results in a relational database — surfacing trends in public opinion over time.

Reddit API (PRAW) data ingestion and filtering

Text preprocessing and sentiment scoring pipeline

MySQL database storage with structured schema

Trend visualization and subreddit comparison

Python PRAW MySQL VADER Pandas Matplotlib

Movie Recommendation System

Hybrid ML-Powered Recommendation Engine

A hybrid recommendation engine combining collaborative filtering, content-based filtering, and matrix factorization techniques to generate personalized movie suggestions. Evaluated on the MovieLens dataset with multiple ranking metrics.

Collaborative & content-based filtering combined

Matrix factorization with SVD decomposition

Evaluation with RMSE, Precision@K, and NDCG metrics

Interactive query interface for personalized results

Python Scikit-learn Pandas SVD MovieLens NumPy

More projects on GitHub

View All Projects

04. contact

Get In Touch

Have a project in mind? Let's talk.

I'm currently open to new opportunities, collaborations, and interesting projects. Whether you have a question, a proposal, or just want to say hi — my inbox is always open.

Email biplov@biplovsingh.dev

GitHub github.com/biplovsingh

LinkedIn linkedin.com/in/biplovsingh