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algofromscratch

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Implementing ML Algorithms from scratch

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# ScratchML: ML Algorithms from Scratch
 
This repo is a growing collection of scratch implementations of the most common conventional machine learning algorithms — built without relying on high-level libraries like scikit-learn.

Each algorithm is written with clarity and learning in mind, making it easier to understand what’s happening under the hood.

🚀 What's Inside (so far)
✅ Linear Regression

✅ Logistic Regression

✅ K-Nearest Neighbors (KNN)

✅ Decision Tree

✅ Random Forest

✅ Principal Component Analysis (PCA)

✅ K-Means Clustering

More coming soon...

📌 Why this repo?
If you're learning ML or mentoring others, you'll know how important it is to truly understand the core logic behind each algorithm. This repo is my way of reinforcing that understanding — and helping others do the same.

## How to Use
Clone the repo and run any file directly — no extra dependencies required beyond the basics (numpy, matplotlib, sklearn for datasets).

git clone https://github.com/your-username/scratchml.git
cd scratchml
python linear_regression.py

# 🔄 Still a Work in Progress
I'll keep adding more algorithms and improving the existing ones — feel free to follow along, give feedback, or even contribute if you're into this kind of hands-on ML learning!

🙌 Stay connected
If you're learning ML from a non-tech background or transitioning into the field, feel free to connect with me on LinkedIn — always happy to help!

algofromscratch

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