mostafa-toolkit 0.1.0

A professional machine learning and data science toolkit

🚀 Mostafa Toolkit

A Comprehensive Python Toolkit for Machine Learning & Data Science

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

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✨ 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

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📈 Visualization

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

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🤖 Machine Learning Evaluation

Classification

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

Regression

• MAE
• RMSE
• MSE
• R² Score

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⚡ Model Optimization

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

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🚀 Deployment

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

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🧰 Utilities

• Memory optimization
• Helper utilities
• Deep Learning callbacks

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📦 Installation

Clone the repository

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

Go to the project

cd mostafa_toolkit

Install

pip install -e .

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⚡ Quick Start

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()

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📂 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

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📚 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

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🔥 Included Libraries

Mostafa Toolkit already exposes commonly used libraries.

from mostafa_toolkit import *

Includes

• pandas
• numpy
• matplotlib
• seaborn

Machine Learning Models

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

No additional imports required.

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🎯 Typical Workflow

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

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📖 Documentation

Detailed documentation is available in

• USER_GUIDE.md
• API_REFERENCE.md

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🤝 Contributing

Contributions are welcome.

Feel free to open Issues or Pull Requests.

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📄 License

This project is licensed under the MIT License.

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👨‍💻 Author

Mostafa Ali

Computer Science & Artificial Intelligence Student

Machine Learning Engineer

GitHub:

https://github.com/Mostafaali10

LinkedIn:

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

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