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⚙️ Data Science & Machine Learning Portfolio

"It's not enough to just call .fit()."

This repository documents my journey in Machine Learning. Instead of just using libraries blindly, I am writing algorithms from scratch to understand the math, then implementing them in TensorFlow and Scikit-Learn to master the optimization.

📂 Featured Projects

❤️ Heart Disease Classification

A classification project to predict whether a patient has heart disease based on medical attributes.

  • Goal: Classify patients as having heart disease or not.
  • Tech: Scikit-Learn, Pandas, Matplotlib.
  • Location: /project_heart_disease

📚 Library Foundations

I firmly believe in mastering the basics. These folders contain my detailed study notebooks for core Python libraries:

  • NumPy: /numpy - Linear algebra and array manipulation.
  • Pandas: /pandas - Data manipulation and analysis.
  • Matplotlib: /matplotlib - Data visualization.
  • Scikit-Learn: /scikitlearn - ML algorithms and preprocessing.

🏆 Kaggle & External Notebooks

I also publish experiments and competition notebooks on Kaggle. Check them out here:

| My Kaggle Profile | Check out all my datasets and kernels | View Profile |

🚀 How to Run

  1. Clone the repository:

    git clone [https://github.com/ratishgurav/Data_Science-Machine_Learning.git](https://github.com/ratishgurav/Data_Science-Machine_Learning.git)
cd Data_Science-Machine_Learning

  2. Install dependencies:

    pip install pandas numpy matplotlib scikit-learn jupyter

  3. Launch Jupyter Notebook:

    jupyter notebook