pyezml 0.1.0

Beginner-friendly AutoML library for tabular data

Here you go bro — ONE single copy-paste README file. Just create README.md and paste everything below. 🚀

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# ezml 🚀  
**Beginner-Friendly AutoML for Tabular Data**

ezml is a lightweight, easy-to-use AutoML library that lets anyone train machine learning models in just a few lines of code — no deep ML knowledge required.

Built for students, developers, analysts, and beginners who want fast, reliable predictions without complex pipelines.

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## ✨ Key Features

- Smart task detection (classification vs regression)  
- Fast vs Best model modes  
- Automatic preprocessing (missing values + encoding)  
- One-line training helper  
- Dict-based safe prediction  
- Built-in save & load support  
- Lightweight and beginner-friendly  
- Modular and extensible design  

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## Installation

Clone the repository and install dependencies:

```bash
pip install -r requirements.txt

Optional (recommended for best mode):

pip install lightgbm

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⚡ Quick Start (Recommended)

One-line training

from ezml import train_model

model = train_model("data.csv", target="price")

prediction = model.predict([[3000, 3, 2, 5]])

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🔧 Advanced Usage

Using AutoModel directly

from ezml import AutoModel

model = AutoModel(mode="best")  # fast | best
model.train("data.csv", target="price")

preds = model.predict([[3000, 3, 2, 5]])

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⚡ Model Modes

ezml provides two performance modes to balance speed and accuracy.

🚀 fast (default)

Model: RandomForest

Best for: small to medium datasets, quick and stable baseline

Why use it: fast training, very robust, beginner-safe

🔥 best

Model: LightGBM

Best for: larger datasets and higher accuracy needs

Why use it: more powerful learning, better performance on complex tabular data

💡 ezml automatically falls back to RandomForest if LightGBM is not installed.

ezml automatically falls back to RandomForest if LightGBM is not installed.

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🧠 Supported Tasks

ezml automatically detects:

• ✅ Regression problems
• ✅ Binary classification
• ✅ Multi-class classification

Manual override is also supported:

AutoModel(task="classification")
AutoModel(task="regression")

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🔮 Flexible Prediction Inputs

ezml accepts multiple input formats:

Dict (recommended)

model.predict({
    "feature1": value1,
    "feature2": value2
})

List

model.predict([[v1, v2, v3]])

Batch dict

model.predict([
    {"feature1": v1, "feature2": v2},
    {"feature1": v3, "feature2": v4}
])

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💾 Save and Load Models

Save

model.save("model.pkl")

Load

from ezml import AutoModel

loaded_model = AutoModel.load("model.pkl")
preds = loaded_model.predict({...})

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🧹 Automatic Preprocessing

ezml automatically handles:

• Missing value imputation
• Categorical encoding
• Optional feature scaling
• Column alignment during prediction

No manual preprocessing required.

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🎯 Project Goal

ezml aims to make machine learning:

• simple
• fast
• accessible
• beginner-friendly

without sacrificing real-world usability.

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🤝 Contributing

Contributions, issues, and suggestions are welcome!

If you find a bug or have an idea:

1. Fork the repo
2. Create a feature branch
3. Submit a pull request

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⭐ Support

If you find ezml useful, consider giving the repo a star ⭐

It helps the project grow!