About Me • Tech Stack & Skills • GitHub Trophies • Featured Projects • GitHub Stats • Let's Connect!
- 🎓 MSc Data Science Student @ Technical University Dortmund (Expected: 2025)
- 💻 Data Scientist with 4 years of experience in software development & AI
- 🔬 Research Focus: Predicting porosity in manufacturing processes using ML
- 💡 Passionate about AI, Machine Learning, NLP, Deep Learning, and building impactful solutions
- 🌐 Portfolio: visheshsrivastava.com
“Strive not to be a success, but rather to be of value.” – Albert Einstein
- I love tinkering with new AI tools & frameworks.
- I enjoy reading about quantum computing and futuristic technology.
- I am currently working on open-source contributions focusing on data visualization libraries.
Programming Languages:
- Python
- R
- C#
- SQL
- Java
AI/ML Frameworks:
- TensorFlow
- PyTorch
- LangChain
- Transformers
Tools & Services:
- Docker
- Git
- ElasticSearch
- OpenAI API
Cloud Platforms:
- AWS
- Azure
Data Science Expertise:
- Predictive Modeling
- NLP
- Time Series Analysis
- Generative AI
- IEEE Publication on Anomaly Detection using Autoencoder Optimization.
- Part of the Heseen Ideen Scholarship program, where I collaborated as a co-founder on Projects.ai, an innovative AI-driven tool designed for project automation.
| Project | Description | Tech Stack | Repo |
|---|---|---|---|
| Sustainability Score Evaluator | Built a web app for real-time sustainability scoring | C#, ASP.NET, Google API | Repo Link |
| Domain-Specific Chatbot | GPT-2 & GPT-3 chatbot for event-driven tasks | Python, OpenAI APIs | Repo Link |
| Autoencoder Optimization | Anomaly detection techniques using TensorFlow (IEEE Published) | Python, TensorFlow | Repo Link |
| Online Boat Rental Service | Scalable platform for online boat rentals | JavaScript, ASP.NET, SQL | Repo Link |
Python Snippet: Quick Data Load & Preprocessing
import pandas as pd
import numpy as np
# Load dataset
df = pd.read_csv('data.csv')
# Simple cleaning
df.dropna(inplace=True)
df['date'] = pd.to_datetime(df['date'])
# Feature engineering
df['year'] = df['date'].dt.year
df['log_value'] = np.log1p(df['value'])
# Quick summary
print(df.describe())SQL Snippet: Creating a Table
CREATE TABLE Sales (
SaleID INT PRIMARY KEY,
ProductName VARCHAR(100),
Quantity INT,
Price DECIMAL(10, 2),
SaleDate DATE
);
INSERT INTO Sales (SaleID, ProductName, Quantity, Price, SaleDate)
VALUES (1, 'Laptop', 5, 999.99, '2024-01-15');
In Progress
(Coming soon on visheshsrivastava.com)
⭐ Thank you for visiting! Feel free to explore my work and connect.
Let’s build something great together! 🚀