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About Me:

🔭Wanderer of the cosmos, weaving constellations with words, pens and codes. I distill universes of data and algorithms, forging galaxies of `knowledge`.

Socials:

LinkedIn

Tech Stack:

LaTeX R Python Solidity Azure Google Cloud AWS Postgres MySQL Keras NumPy Pandas Plotly PyTorch scikit-learn SciPy TensorFlow Docker Kubernetes

GitHub Stats:


Santiago Canepa's Projects

dapp-back-end icon dapp-back-end

The repositorie presents a sophisticated architecture for a decentralized financial ecosystem, EmpowerAction. It focuses on the dynamics of a decentralized job market, exchanging through cryptographic assets and credit mechanisms.

financial-credits-framework- icon financial-credits-framework-

The paper presents a comprehensive framework for our decentralized EmpowerAction economy. It focuses on the structuring and regulation of supply and demand pricing, balances, issuance, and circulation of tokens, as well as credit management within the system.

insta_bot icon insta_bot

AI-powered Instagram bot for precise gender targeting using XGBoost and OpenAI ADA, with 91% accuracy at just $0.001 per 1000 queries. Automates follows/unfollows from user lists or photo likes, and checks follow-backs with randomized human-like actions. Ideal for influencers and marketers aiming for targeted engagement.

instagrambot icon instagrambot

Selenium automation for IG: Go on, Check a bollowers, Stop following

sentiment-analysis-using-lstm---pytorchv3 icon sentiment-analysis-using-lstm---pytorchv3

This Jupyter notebook presents a simple implementation of a Sentiment Analysis model. The model is trained on the IMDB dataset, which contains 50,000 movie reviews labeled as either positive or negative. We use PyTorch to build and train a recurrent neural network (RNN) with long short-term memory (LSTM) cells as our machine learning model.

target_gender icon target_gender

Target Gender | Prediction to username for various social networks

twitter-sentimental-analysis-2022 icon twitter-sentimental-analysis-2022

Detailed Twitter sentiment analysis project. Machine learning and natural language processing techniques are used to classify tweets into emotional categories.

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