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Hey! Nice to see you.

Welcome to my page!
I'm Arnab, Machine Learning Engineer from Tripura, India

My Tech Stack:

Python C++ HTML5 CSS3 JavaScript Jupyter MySQL PowerBI PySpark OpenCV TensorFlow Keras PyTorch PyGame YOLO Scikit-learn Pandas NumPy OpenAI OpenAI Gym LangChain AWS Git GitHub Actions Oracle Cloud Infrastructure Adobe Photoshop Adobe Illustrator Adobe Premiere Pro Adobe After Effects

starsq Things I Love to Learn:

  • 👁️ Computer Vision
  • 🧠 Deep Learning
  • 🤖 Reinforcement Learning
  • Generative AI

Typing Cat What I'm Up To:

- 🧩 Building Applications on GenAI
- 🔮 Exploring Deep Reinforcement Learning

Connect with me!

sonic Github Stats:

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Arnab Saha's Projects

customer-churn_prediction---analysis icon customer-churn_prediction---analysis

Predict customer churn using machine learning. This project employs a RandomForestClassifier to analyze customer data and determine the likelihood of churn. Explore the Jupyter Notebook for insights into the data and model, and contribute to the project's development.

datahelix-encoding-decoding icon datahelix-encoding-decoding

DataHelix is a Python tool for encoding any dataset into DNA sequences and decoding them back when necessary, aiming to reduce storage space required for large data

heart-attack-prediction-data-analysis icon heart-attack-prediction-data-analysis

Explore heart attack patterns, risk factors, and predictive modeling using Python and PySpark. Cleanse and analyze large datasets with efficiency, conduct exploratory data analysis, and deploy machine learning for predictions. Ideal for personal exploration and customization.

piezoelectric-roads-implementation icon piezoelectric-roads-implementation

This repository implements a piezoelectric road system in Python, leveraging Pandas, NumPy, scikit-learn, Matplotlib, and Seaborn. The requirements.txt file ensures version consistency for reproducibility.

starbucks-data-analysis icon starbucks-data-analysis

Starbucks Data Analysis with Python and PySpark for robust insights. Explore customer preferences and sales trends efficiently, leveraging the power of big data processing.

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