Metadata-Version: 2.4
Name: mostafa_toolkit
Version: 0.1.0
Summary: A professional machine learning and data science toolkit
Author-email: Mostafa Ali <mostafaali9976@gmail.com>
License: MIT
Keywords: machine-learning,data-science,eda,python,visualization,classification,regression
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Requires-Dist: imbalanced-learn
Requires-Dist: xgboost
Requires-Dist: lightgbm
Requires-Dist: catboost
Requires-Dist: tensorflow
Requires-Dist: joblib
Requires-Dist: shap
Requires-Dist: tqdm
Requires-Dist: ipython
Dynamic: license-file

<div align="center">

# 🚀 Mostafa Toolkit

### A Comprehensive Python Toolkit for Machine Learning & Data Science

[![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)]()
[![License](https://img.shields.io/badge/License-MIT-green.svg)]()
[![Version](https://img.shields.io/badge/version-0.1.0-orange.svg)]()

A powerful, beginner-friendly, and production-ready toolkit that simplifies the entire Machine Learning workflow.

</div>

---

# ✨ Features

Mostafa Toolkit provides a unified API for common Machine Learning tasks.

## 📊 Exploratory Data Analysis (EDA)

- Statistical dataset analysis
- Missing value inspection
- Duplicate detection
- Data type analysis
- Cardinality analysis
- Numerical summaries

---

## 📈 Visualization

- Distribution plots
- Boxplots (Outlier Detection)
- Categorical plots
- Correlation Heatmaps
- Target Distribution
- Deep Learning Learning Curves

---

## 🤖 Machine Learning Evaluation

### Classification

- Accuracy
- Precision
- Recall
- F1 Score
- Confusion Matrix
- ROC Curve

### Regression

- MAE
- RMSE
- MSE
- R² Score

---

## ⚡ Model Optimization

- Baseline model benchmarking
- Custom Random Search
- Feature Importance
- Hyperparameter Optimization

---

## 🚀 Deployment

- Export trained models
- Save preprocessing pipelines
- Deployment-ready assets

---

## 🧰 Utilities

- Memory optimization
- Helper utilities
- Deep Learning callbacks

---

# 📦 Installation

Clone the repository

```bash
git clone https://github.com/YOUR_USERNAME/mostafa_toolkit.git
```

Go to the project

```bash
cd mostafa_toolkit
```

Install

```bash
pip install -e .
```

---

# ⚡ Quick Start

```python
from mostafa_toolkit import *

# Load dataset
df = pd.read_csv("house_prices.csv")

# EDA
statistical_analysis(df)

# Visualization
plot_correlation(df)

# Create model
model = RandomForestRegressor()
```

---

# 📂 Project Structure

```
mostafa_toolkit/
│
├── README.md
├── LICENSE
├── pyproject.toml
├── requirements.txt
│
├── docs/
│
├── examples/
│
├── tests/
│
└── mostafa_toolkit/
    ├── __init__.py
    ├── imports.py
    ├── constants.py
    ├── config.py
    ├── eda.py
    ├── visualization.py
    ├── evaluation.py
    ├── optimization.py
    ├── deployment.py
    ├── callbacks.py
    └── utils.py
```

---

# 📚 Modules

| Module | Description |
|---------|-------------|
| `eda.py` | Exploratory Data Analysis |
| `visualization.py` | Data Visualization |
| `evaluation.py` | Model Evaluation |
| `optimization.py` | Hyperparameter Optimization |
| `deployment.py` | Export Models |
| `callbacks.py` | Deep Learning Callbacks |
| `utils.py` | Utility Functions |
| `imports.py` | Frequently Used Libraries |
| `constants.py` | Shared Constants |
| `config.py` | Global Configuration |

---

# 🔥 Included Libraries

Mostafa Toolkit already exposes commonly used libraries.

```python
from mostafa_toolkit import *
```

Includes

- pandas
- numpy
- matplotlib
- seaborn

Machine Learning Models

- LinearRegression
- LogisticRegression
- DecisionTree
- RandomForest
- GradientBoosting
- KNN
- SVM
- XGBoost

No additional imports required.

---

# 🎯 Typical Workflow

```
Load Dataset
      │
      ▼
EDA
      │
      ▼
Visualization
      │
      ▼
Preprocessing
      │
      ▼
Training
      │
      ▼
Evaluation
      │
      ▼
Optimization
      │
      ▼
Deployment
```

---

# 📖 Documentation

Detailed documentation is available in

- USER_GUIDE.md
- API_REFERENCE.md

---

# 🤝 Contributing

Contributions are welcome.

Feel free to open Issues or Pull Requests.

---

# 📄 License

This project is licensed under the MIT License.

---

# 👨‍💻 Author

**Mostafa Ali**

Computer Science & Artificial Intelligence Student

Machine Learning Engineer

GitHub:

https://github.com/Mostafaali10

LinkedIn:

https://www.linkedin.com/in/mostafa-ali10/

---

⭐ If you find this project useful, don't forget to Star the repository.
