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KalooIna/README.md

Hello World, I am Kaloo Ina !

📜 About me

An eternal learner with a passion for tackling complex problems in machine learning and AI. I am a data scientist who is expanding expertise in advanced ML architectures. I approach challenges methodically, enjoy experimenting with new approaches, and strive to learn along the way.
🔭 Currently working on and open to collaborating on : Gen AI projects for research and experimentation.
📚 I'm currently learning : Optimization techniques, and efficient data engineering workflows.
🤝 I'm looking for help with : Remembering why I wrote certain lines of code in the first place.
💬 Ask me about : The moment I realized the computer was right and I was wrong …
⚡ Fun fact : My code works perfectly ! Until someone looks at it … Then it behaves like a photon shying away from too much attention.

💻 Tech Stack

Python MySQL R C C# C++ PowerShell Windows Terminal AWS Azure Anaconda Streamlit Neo4J MicrosoftSQLServer Keras Matplotlib NumPy Plotly Pandas PyTorch scikit-learn Scipy TensorFlow GitHub Git HTML5 CSS3 Blender Adobe Krita

🚀 My workflow & Github stats 📊

  • Step 1 : Decide to “quickly test an idea”, fully aware that this is how every major detour of my life begins.
  • Step 2 : Create a clean environment, a neat folder structure, and a beautifully organized plan.
    This is the last moment of order before chaos arrives.
  • Step 3 : Load the dataset with confidence.
    Immediately discover three columns with 65% NAs, two cursed values, and something that statistically counts as a supernatural event.
  • Step 4 : Begin debugging.
    My debugging strategy is simple : stare at the problem until it becomes scared and fixes itself.
  • Step 5 : Try to run the preprocessing pipeline.
    I spend most of my time convincing computers to cooperate using logic, patience, and the occasional heavy sigh.
  • Step 6 : Train the first model.
    It performs so badly that even the loss function hesitates to report the number out of embarrassment.
  • Step 7 : Fine-tune, refactor, tweak hyperparameters, rewrite half the codebase “just to test something”.
    I specialize in turning “this should not happen” into “well, it works now … somehow”.
  • Step 8 : Evaluate, write the README, clean up experiments, and pretend it was all smooth and intentional.
    I consider it a good day when my code runs and I do not have to question my entire career path.

✍️ Random Dev Quote

Pinned Loading

  1. cybersecurity_attacks cybersecurity_attacks Public

    Python 1 4

  2. penguins_analysis_ML penguins_analysis_ML Public

    R