https://github.com/codestrate/miniml_toolkit
https://github.com/codestrate/miniml_toolkit
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/codestrate/miniml_toolkit
- Owner: CodeStrate
- Created: 2025-07-11T17:31:50.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-07-15T06:59:15.000Z (8 months ago)
- Last Synced: 2025-07-15T15:05:43.856Z (8 months ago)
- Language: Python
- Size: 6.84 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 MiniML-Toolkit — Machine Learning Using Only NumPy and Pure Math
Welcome to **MiniML-Toolkit**, a DIY machine learning sandbox where we rebuild models from scratch using nothing but **NumPy** and a love for math.
No scikit-learn. No black boxes. Just gradients, dot products, and clean Python code.
---
## 📦 Features
✅ Simple Linear Regression (1 feature)
✅ Multiple Linear Regression (n features)
✅ Gradient Descent optimizer
✅ Custom Standard Scaler
✅ Manual Train/Test Split
✅ R², MSE, RMSE Metrics — from scratch
---
## 🧠 Project Structure
```
MiniML-Toolkit/
├── models/
│ ├── simple_linear_regression.py # SLR model (1 X, 1 y)
│ └── multiple_linear_regression.py # MLR model (n X, 1 y)
│ └── and more !!!
├── utils/
│ ├── standard_scaler.py # Custom StandardScaler class
│ └── model_utils.py # Normalizer and Train Test Splitter
├── app.py # Run MLR/SLR training + evaluation
├── requirements.txt
└── README.md
````
---
## 🛠️ Installation
### 📌 Using `venv` (Recommended)
```bash
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
````
### 📌 Or use `conda`
```bash
conda create -n miniml python= -y
conda activate miniml
pip install -r requirements.txt
```
---
## ▶️ How to Run
```bash
python app.py
```
modify `app.py` directly to experiment with parameters, datasets, and iterations.
---
## 📈 Sample Output
```text
[MLR] Initial MSE: 22.14
[MLR] Final RMSE: 4.11 | R²: 0.704
[SLR] Initial MSE: 33.8
[SLR] Final RMSE: 5.47 | R²: 0.52
```
---
## 💬 Why This Exists
Because nothing beats learning ML like writing it from scratch.
This toolkit is about understanding **how** things work, not just *that* they work.
More models (logistic regression, neural nets, etc.) coming soon!
---
## 🧪 Requirements
```bash
numpy
pandas
loguru
# optionally add matplotlib , seaborn for visuals
```
Install via:
```bash
pip install -r requirements.txt
```
---
## 🤝 Contributing
Ideas? Fixes? New models?
Open an issue or PR — this project is open to all math-loving developers.
---
## 📜 License
MIT. Use it, learn from it, and feel free to build your own toolkit on top!