⚙️ 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