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👋 Hi, I’m @dhruvthakur2000 AKA PsyRar👋

###👋 Hey there! Welcome to my GitHub profile, where I explore the exciting world of machine learning and artificial intelligence. I am passionate about AI, data science, and the endless possibilities they offer.I just graduated in electronics and communication with proficiency in machine learning. I was introduced to world of machine learning in my academics and I have been constantly increasing my knowledge on the subject eversince.

PINS:

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Pandas

  • 👀 Open source, machine learning , python
  • 🌱 I’m currently coding in python,learning in the process ing the process machine learning and neural networks
  • 💞️ I’m looking to collaborate on data science projects
  • 📫 How to reach me ...

Dhruv Thakur's Projects

ddos-attack-detection-and-mitigation icon ddos-attack-detection-and-mitigation

SDN networks (Software Defined Networking ) are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML)

flaml icon flaml

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

ml-project-drug-review-dataset icon ml-project-drug-review-dataset

This is an innovative machine learning project that utilizes patient reviews with many other attributes to analyze and evaluate the effectiveness of drugs.

pykitzoid icon pykitzoid

This is a repository containing a package coded in Go language, with the motive of providing ML support.

redditflow icon redditflow

Do everything from data collection from reddit to training a machine learning model in just two lines of python code!

salary_proj icon salary_proj

repository for my first project i.e salary prediction of a data scientist

scikit-learn_bench icon scikit-learn_bench

scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms.

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