| Name | noventis JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | https://github.com/bccfilkom/noventis |
| Summary | An all-in-one automation library that simplifies data cleaning, exploratory data analysis, and machine learning — from raw data to ready-to-deploy models. |
| upload_time | 2025-10-10 16:33:42 |
| maintainer | None |
| docs_url | None |
| author | Richard, Fatoni Murfid Syafii, Ahmad Nafi Mubarok, Orie Abyan Maulana, Grace Wahyuni, Rimba Nevada, Alexander Angelo, Jason Surya Winata, Nada Musyaffa Bilhaqi |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
machine learning
automl
automated machine learning
data science
artificial intelligence
data cleaning
feature engineering
eda
exploratory data analysis
predictor
scikit-learn
xgboost
lightgbm
catboost
optuna
shap
flaml
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
pandas
numpy
scipy
matplotlib
seaborn
ipython
scikit-learn
statsmodels
optuna
shap
flaml
xgboost
lightgbm
catboost
category_encoders
joblib
imbalanced-learn
Jinja2
nbformat
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<h1 align="center">
<img src="https://github.com/user-attachments/assets/8d64296a-55f2-4eb4-bc55-275f5d75ef75" alt="Noventis Logo" width="40" height="40" style="vertical-align: middle;"/>
Noventis
</h1>
### Intelligent Automation for Your Data Analysis
[](https://badge.fury.io/py/noventis)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[Website](https://noventis-fe.vercel.app/) • [Documentation](https://github.com/bccfilkom/noventis)
<img width="1247" height="637" alt="Screenshot From 2025-10-02 09-44-31" src="https://github.com/user-attachments/assets/264f13ce-4f5a-477a-a89d-73f0c9a585bd" />
</div>
---
## 🚀 Overview
**Noventis** is a powerful Python library designed to revolutionize your data analysis workflow through intelligent automation. Built with modern data scientists and analysts in mind, Noventis provides cutting-edge tools for automated exploratory data analysis, predictive modeling, and data cleaning—all with minimal code.
### ✨ Key Features
- **🔍 EDA Auto** - Automated exploratory data analysis with comprehensive visualizations and statistical insights
- **🎯 Predictor** - Intelligent ML model selection and training with automated hyperparameter tuning
- **🧹 Data Cleaner** - Smart data preprocessing and cleaning with advanced imputation strategies
- **⚡ Fast & Efficient** - Optimized for performance with large datasets
- **📊 Rich Visualizations** - Beautiful, publication-ready charts and reports
- **🔧 Highly Customizable** - Fine-tune every aspect to match your needs
---
## 📦 Installation
### Quick Installation
```bash
pip install noventis
```
### Install from Source
```bash
git clone https://github.com/yourusername/noventis.git
cd noventis
pip install -e .
```
### Verify Installation
```python
import noventis
print(noventis.__version__)
noventis.print_info() # Show detailed installation info
```
---
## 🎯 Quick Start
### 1️⃣ Data Cleaner
Get started with intelligent data preprocessing and cleaning.
```python
import pandas as pd
from noventis.data_cleaner import AutoCleaner
# Load your data
df = pd.read_csv('your_data.csv')
# Automatic data cleaning
cleaner = AutoCleaner()
df_clean = cleaner.fit_transform(df)
# The cleaned data is ready for analysis!
print(df_clean.info())
```
👉 [Read the Data Cleaner Guide](https://github.com/bccfilkom/noventis/blob/main/docs/data_cleaner.md)
### 2️⃣ EDA Auto
Automatically generate comprehensive exploratory data analysis reports.
```python
from noventis.eda_auto import EDAuto
# Create EDA report
eda = EDAuto(df_clean)
# Generate comprehensive analysis
eda.generate_report()
# Show specific analyses
eda.show_distributions()
eda.show_correlations()
eda.show_missing_patterns()
```
👉 [Read the EDA Auto Guide](https://github.com/bccfilkom/noventis/blob/main/docs/eda_auto.md)
### 3️⃣ Predictor
Build and train machine learning models with automated optimization.
```python
from noventis.predictor import PredictorAuto
# Prepare data
X = df_clean.drop('target', axis=1)
y = df_clean['target']
# Automatic model training
predictor = PredictorAuto()
predictor.fit(X, y, task='classification')
# Make predictions
predictions = predictor.predict(X_test)
# Get model performance
print(predictor.get_metrics())
```
[Read the Predictor Guide →](https://github.com/bccfilkom/noventis/blob/main/docs/predictor.md)
### 4️⃣ Complete Pipeline Example
```python
import pandas as pd
from noventis.data_cleaner import AutoCleaner
from noventis.eda_auto import EDAuto
from noventis.predictor import PredictorAuto
# 1. Load data
df = pd.read_csv('your_data.csv')
# 2. Clean data
cleaner = AutoCleaner()
df_clean = cleaner.fit_transform(df)
# 3. Explore data
eda = EDAuto(df_clean)
eda.generate_report()
# 4. Train model
X = df_clean.drop('target', axis=1)
y = df_clean['target']
predictor = PredictorAuto()
predictor.fit(X, y, task='classification')
# 5. Evaluate
print(f"Model Accuracy: {predictor.score(X_test, y_test):.2%}")
```
---
## 📚 Core Modules
### 🧹 Data Cleaner
Intelligent data preprocessing and cleaning with advanced strategies:
- **Missing Data Handling** - Multiple imputation strategies (mean, median, KNN, iterative)
- **Outlier Treatment** - Statistical and ML-based detection (IQR, Z-score, Isolation Forest)
- **Feature Scaling** - Normalization and standardization techniques
- **Encoding** - Automatic categorical variable encoding (One-Hot, Label, Target)
- **Data Type Detection** - Intelligent type inference and conversion
- **Duplicate Removal** - Smart duplicate detection and handling
[Learn more →](docs/data_cleaner.md)
### 🔍 EDA Auto
Comprehensive exploratory data analysis automation:
- **Statistical Summary** - Descriptive statistics for all features
- **Distribution Analysis** - Histograms, KDE plots, and normality tests
- **Correlation Analysis** - Heatmaps and correlation matrices
- **Missing Data Analysis** - Visualization and patterns of missing values
- **Outlier Detection** - Automatic identification of anomalies
- **Feature Relationships** - Scatter plots and pairwise analysis
[Learn more →](docs/eda_auto.md)
### 🎯 Predictor
Automated machine learning with intelligent model selection:
- **Auto Model Selection** - Automatically selects the best algorithm for your data
- **Hyperparameter Tuning** - Optimizes model parameters using advanced search algorithms
- **Feature Engineering** - Creates and selects relevant features automatically
- **Cross-Validation** - Robust model evaluation with k-fold validation
- **Model Explainability** - SHAP values and feature importance analysis
- **Ensemble Methods** - Combines multiple models for better performance
**Supported Algorithms:**
- Scikit-learn: Random Forest, Gradient Boosting, Logistic Regression, SVM
- XGBoost: Extreme Gradient Boosting
- LightGBM: Light Gradient Boosting Machine
- CatBoost: Categorical Boosting
- And many more...
[Learn more →](docs/auto.md)
---
## 🛠️ Requirements
### System Requirements
- Python 3.8 or higher
- 4GB RAM minimum (8GB+ recommended for large datasets)
- Windows, macOS, or Linux
### Core Dependencies
Noventis automatically installs these dependencies:
- **Data Processing**: pandas, numpy, scipy
- **Visualization**: matplotlib, seaborn
- **Machine Learning**: scikit-learn, xgboost, lightgbm, catboost
- **AutoML**: optuna, flaml, shap
- **Feature Engineering**: category_encoders, statsmodels
See [requirements.txt](requirements.txt) for complete list.
---
## 🤝 Contributing
We welcome contributions from the community! Here's how you can help:
### Ways to Contribute
1. **🐛 Report Bugs** - Found a bug? [Open an issue](https://github.com/yourusername/noventis/issues)
2. **💡 Suggest Features** - Have ideas? We'd love to hear them!
3. **📖 Improve Documentation** - Help us make the docs better
4. **🔧 Submit Pull Requests** - Fix bugs or add features
### Development Setup
```bash
# Clone the repository
git clone https://github.com/yourusername/noventis.git
cd noventis
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install in development mode
pip install -e .[dev]
# Run tests
pytest tests/
# Run linting
flake8 noventis/
black noventis/
```
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.
---
## 👥 Contributors
This project exists thanks to all the people who contribute:
| Contributor | Role |
| ------------------------- | ------------------- |
| **Richard** | Product Manager |
| **Fatoni Murfids** | AI Product Manager |
| **Ahmad Nafi Mubarok** | Lead Data Scientist |
| **Orie Abyan Maulana** | Lead Data Analyst |
| **Grace Wahyuni** | Data Analyst |
| **Alexander Angelo** | Data Scientist |
| **Rimba Nevada** | Data Scientist |
| **Jason Surya Winata** | Frontend Engineer |
| **Nada Musyaffa Bilhaqi** | Product Designer |
### Special Thanks
A huge thank you to the maintainers of our dependencies:
- pandas, numpy, scikit-learn, and the entire Python scientific computing community
- XGBoost, LightGBM, and CatBoost teams for excellent gradient boosting libraries
- Optuna and FLAML teams for amazing AutoML frameworks
---
## 📂 Project Structure
The folder structure of **Noventis** project:
```bash
.
├── 📁 dataset_for_examples/ # Sample datasets for testing
├── 📁 docs/ # Documentation files
├── 📁 examples/ # Example notebooks and scripts
├── 📁 noventis/ # Main library code
│ ├── 📁 __pycache__/
│ ├── 📁 asset/ # Asset files (if any)
│ ├── 📁 core/ # Core functionality
│ ├── 📁 data_cleaner/ # Data cleaning module
│ │ ├── 📄 __init__.py
│ │ ├── 📄 auto.py
│ │ ├── 📄 data_quality.py
│ │ ├── 📄 encoding.py
│ │ ├── 📄 imputing.py
│ │ ├── 📄 orchestrator.py
│ │ ├── 📄 outlier_handling.py
│ │ └── 📄 scaling.py
│ ├── 📁 eda_auto/ # EDA automation module
│ │ ├── 📄 __init__.py
│ │ └── 📄 eda_auto.py
│ ├── 📁 predictor/ # Prediction module
│ │ ├── 📄 __init__.py
│ │ ├── 📄 auto.py
│ │ └── 📄 manual.py
│ └── 📄 __init__.py # Main package init
├── 📁 noventis.egg-info/ # Package metadata
│ ├── 📄 dependency_links.txt
│ ├── 📄 PKG-INFO
│ ├── 📄 SOURCES.txt
│ └── 📄 top_level.txt
├── 📁 tests/ # Unit tests
├── 📄 .gitignore # Git ignore rules
├── 📄 LICENSE # MIT License
├── 📄 MANIFEST.in # Package manifest
├── 📄 pyproject.toml # Modern Python packaging config
├── 📄 README.md # This file
├── 📄 requirements.txt # Production dependencies
├── 📄 requirements-dev.txt # Development dependencies
└── 📄 setup.py # Package setup script
```
### 📌 Notes
- The `noventis/` folder contains the **main library code**
- The `tests/` folder is dedicated to **unit testing and integration testing**
- `setup.py` and `pyproject.toml` are used for **packaging and distribution**
- `requirements.txt` lists the **external dependencies** needed for the project
🚀 With this structure, the project is ready for development, testing, and publishing on **PyPI or GitHub**.
---
## 🔧 Troubleshooting
### Common Issues
**Problem**: `ModuleNotFoundError: No module named 'noventis'`
```bash
# Solution: Reinstall the package
pip uninstall noventis
pip install noventis
```
**Problem**: Dependencies conflict
```bash
# Solution: Create a fresh virtual environment
python -m venv fresh_env
source fresh_env/bin/activate
pip install noventis
```
**Problem**: Import errors after installation
```python
# Solution: Verify installation
import noventis
print(noventis.__version__)
noventis.print_info() # Check all dependencies
```
### Getting Help
- 📖 [Documentation](https://docs.noventis.dev)
- 🐛 [GitHub Issues](https://github.com/bcc/noventis/issues)
---
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
### Third-Party Licenses
Noventis uses several open-source libraries. We are grateful to their maintainers:
- **Data Processing**: pandas (BSD), numpy (BSD), scipy (BSD)
- **Visualization**: matplotlib (PSF), seaborn (BSD)
- **Machine Learning**: scikit-learn (BSD), xgboost (Apache 2.0), lightgbm (MIT), catboost (Apache 2.0)
- **AutoML**: optuna (MIT), flaml (MIT), shap (MIT)
- **Feature Engineering**: category_encoders (BSD), statsmodels (BSD)
All dependencies are licensed under permissive open-source licenses (BSD, MIT, Apache 2.0).
---
## 📚 Citation
If you use Noventis in your research, please cite:
```bibtex
@software{noventis2025,
author = {Noventis Team},
title = {Noventis: Intelligent Automation for Data Analysis},
year = {2025},
url = {https://github.com/bccfilkom/noventis}
}
```
---
## 🌟 Star History
[](https://star-history.com/#yourusername/noventis&Date)
---
<div align="center">
Made with ❤️ by [Noventis Team](https://noventis.dev)
If you find Noventis useful, please consider giving it a ⭐ on [GitHub](https://github.com/yourusername/noventis)!
Raw data
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"description": "<div align=\"center\">\r\n \r\n<h1 align=\"center\">\r\n <img src=\"https://github.com/user-attachments/assets/8d64296a-55f2-4eb4-bc55-275f5d75ef75\" alt=\"Noventis Logo\" width=\"40\" height=\"40\" style=\"vertical-align: middle;\"/>\r\n Noventis\r\n</h1>\r\n\r\n### Intelligent Automation for Your Data Analysis\r\n\r\n[](https://badge.fury.io/py/noventis)\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MIT)\r\n\r\n[Website](https://noventis-fe.vercel.app/) \u2022 [Documentation](https://github.com/bccfilkom/noventis)\r\n\r\n<img width=\"1247\" height=\"637\" alt=\"Screenshot From 2025-10-02 09-44-31\" src=\"https://github.com/user-attachments/assets/264f13ce-4f5a-477a-a89d-73f0c9a585bd\" />\r\n\r\n</div>\r\n\r\n---\r\n\r\n## \ud83d\ude80 Overview\r\n\r\n**Noventis** is a powerful Python library designed to revolutionize your data analysis workflow through intelligent automation. Built with modern data scientists and analysts in mind, Noventis provides cutting-edge tools for automated exploratory data analysis, predictive modeling, and data cleaning\u2014all with minimal code.\r\n\r\n### \u2728 Key Features\r\n\r\n- **\ud83d\udd0d EDA Auto** - Automated exploratory data analysis with comprehensive visualizations and statistical insights\r\n- **\ud83c\udfaf Predictor** - Intelligent ML model selection and training with automated hyperparameter tuning\r\n- **\ud83e\uddf9 Data Cleaner** - Smart data preprocessing and cleaning with advanced imputation strategies\r\n- **\u26a1 Fast & Efficient** - Optimized for performance with large datasets\r\n- **\ud83d\udcca Rich Visualizations** - Beautiful, publication-ready charts and reports\r\n- **\ud83d\udd27 Highly Customizable** - Fine-tune every aspect to match your needs\r\n\r\n---\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n### Quick Installation\r\n\r\n```bash\r\npip install noventis\r\n```\r\n\r\n### Install from Source\r\n\r\n```bash\r\ngit clone https://github.com/yourusername/noventis.git\r\ncd noventis\r\npip install -e .\r\n```\r\n\r\n### Verify Installation\r\n\r\n```python\r\nimport noventis\r\nprint(noventis.__version__)\r\nnoventis.print_info() # Show detailed installation info\r\n```\r\n\r\n---\r\n\r\n## \ud83c\udfaf Quick Start\r\n\r\n### 1\ufe0f\u20e3 Data Cleaner\r\n\r\nGet started with intelligent data preprocessing and cleaning.\r\n\r\n```python\r\nimport pandas as pd\r\nfrom noventis.data_cleaner import AutoCleaner\r\n\r\n# Load your data\r\ndf = pd.read_csv('your_data.csv')\r\n\r\n# Automatic data cleaning\r\ncleaner = AutoCleaner()\r\ndf_clean = cleaner.fit_transform(df)\r\n\r\n# The cleaned data is ready for analysis!\r\nprint(df_clean.info())\r\n```\r\n\r\n\ud83d\udc49 [Read the Data Cleaner Guide](https://github.com/bccfilkom/noventis/blob/main/docs/data_cleaner.md)\r\n\r\n### 2\ufe0f\u20e3 EDA Auto\r\n\r\nAutomatically generate comprehensive exploratory data analysis reports.\r\n\r\n```python\r\nfrom noventis.eda_auto import EDAuto\r\n\r\n# Create EDA report\r\neda = EDAuto(df_clean)\r\n\r\n# Generate comprehensive analysis\r\neda.generate_report()\r\n\r\n# Show specific analyses\r\neda.show_distributions()\r\neda.show_correlations()\r\neda.show_missing_patterns()\r\n```\r\n\r\n\ud83d\udc49 [Read the EDA Auto Guide](https://github.com/bccfilkom/noventis/blob/main/docs/eda_auto.md)\r\n\r\n### 3\ufe0f\u20e3 Predictor\r\n\r\nBuild and train machine learning models with automated optimization.\r\n\r\n```python\r\nfrom noventis.predictor import PredictorAuto\r\n\r\n# Prepare data\r\nX = df_clean.drop('target', axis=1)\r\ny = df_clean['target']\r\n\r\n# Automatic model training\r\npredictor = PredictorAuto()\r\npredictor.fit(X, y, task='classification')\r\n\r\n# Make predictions\r\npredictions = predictor.predict(X_test)\r\n\r\n# Get model performance\r\nprint(predictor.get_metrics())\r\n```\r\n\r\n[Read the Predictor Guide \u2192](https://github.com/bccfilkom/noventis/blob/main/docs/predictor.md)\r\n\r\n### 4\ufe0f\u20e3 Complete Pipeline Example\r\n\r\n```python\r\nimport pandas as pd\r\nfrom noventis.data_cleaner import AutoCleaner\r\nfrom noventis.eda_auto import EDAuto\r\nfrom noventis.predictor import PredictorAuto\r\n\r\n# 1. Load data\r\ndf = pd.read_csv('your_data.csv')\r\n\r\n# 2. Clean data\r\ncleaner = AutoCleaner()\r\ndf_clean = cleaner.fit_transform(df)\r\n\r\n# 3. Explore data\r\neda = EDAuto(df_clean)\r\neda.generate_report()\r\n\r\n# 4. Train model\r\nX = df_clean.drop('target', axis=1)\r\ny = df_clean['target']\r\n\r\npredictor = PredictorAuto()\r\npredictor.fit(X, y, task='classification')\r\n\r\n# 5. Evaluate\r\nprint(f\"Model Accuracy: {predictor.score(X_test, y_test):.2%}\")\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udcda Core Modules\r\n\r\n### \ud83e\uddf9 Data Cleaner\r\n\r\nIntelligent data preprocessing and cleaning with advanced strategies:\r\n\r\n- **Missing Data Handling** - Multiple imputation strategies (mean, median, KNN, iterative)\r\n- **Outlier Treatment** - Statistical and ML-based detection (IQR, Z-score, Isolation Forest)\r\n- **Feature Scaling** - Normalization and standardization techniques\r\n- **Encoding** - Automatic categorical variable encoding (One-Hot, Label, Target)\r\n- **Data Type Detection** - Intelligent type inference and conversion\r\n- **Duplicate Removal** - Smart duplicate detection and handling\r\n\r\n[Learn more \u2192](docs/data_cleaner.md)\r\n\r\n### \ud83d\udd0d EDA Auto\r\n\r\nComprehensive exploratory data analysis automation:\r\n\r\n- **Statistical Summary** - Descriptive statistics for all features\r\n- **Distribution Analysis** - Histograms, KDE plots, and normality tests\r\n- **Correlation Analysis** - Heatmaps and correlation matrices\r\n- **Missing Data Analysis** - Visualization and patterns of missing values\r\n- **Outlier Detection** - Automatic identification of anomalies\r\n- **Feature Relationships** - Scatter plots and pairwise analysis\r\n\r\n[Learn more \u2192](docs/eda_auto.md)\r\n\r\n### \ud83c\udfaf Predictor\r\n\r\nAutomated machine learning with intelligent model selection:\r\n\r\n- **Auto Model Selection** - Automatically selects the best algorithm for your data\r\n- **Hyperparameter Tuning** - Optimizes model parameters using advanced search algorithms\r\n- **Feature Engineering** - Creates and selects relevant features automatically\r\n- **Cross-Validation** - Robust model evaluation with k-fold validation\r\n- **Model Explainability** - SHAP values and feature importance analysis\r\n- **Ensemble Methods** - Combines multiple models for better performance\r\n\r\n**Supported Algorithms:**\r\n\r\n- Scikit-learn: Random Forest, Gradient Boosting, Logistic Regression, SVM\r\n- XGBoost: Extreme Gradient Boosting\r\n- LightGBM: Light Gradient Boosting Machine\r\n- CatBoost: Categorical Boosting\r\n- And many more...\r\n\r\n[Learn more \u2192](docs/auto.md)\r\n\r\n---\r\n\r\n## \ud83d\udee0\ufe0f Requirements\r\n\r\n### System Requirements\r\n\r\n- Python 3.8 or higher\r\n- 4GB RAM minimum (8GB+ recommended for large datasets)\r\n- Windows, macOS, or Linux\r\n\r\n### Core Dependencies\r\n\r\nNoventis automatically installs these dependencies:\r\n\r\n- **Data Processing**: pandas, numpy, scipy\r\n- **Visualization**: matplotlib, seaborn\r\n- **Machine Learning**: scikit-learn, xgboost, lightgbm, catboost\r\n- **AutoML**: optuna, flaml, shap\r\n- **Feature Engineering**: category_encoders, statsmodels\r\n\r\nSee [requirements.txt](requirements.txt) for complete list.\r\n\r\n---\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions from the community! Here's how you can help:\r\n\r\n### Ways to Contribute\r\n\r\n1. **\ud83d\udc1b Report Bugs** - Found a bug? [Open an issue](https://github.com/yourusername/noventis/issues)\r\n2. **\ud83d\udca1 Suggest Features** - Have ideas? We'd love to hear them!\r\n3. **\ud83d\udcd6 Improve Documentation** - Help us make the docs better\r\n4. **\ud83d\udd27 Submit Pull Requests** - Fix bugs or add features\r\n\r\n### Development Setup\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/yourusername/noventis.git\r\ncd noventis\r\n\r\n# Create virtual environment\r\npython -m venv venv\r\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\r\n\r\n# Install in development mode\r\npip install -e .[dev]\r\n\r\n# Run tests\r\npytest tests/\r\n\r\n# Run linting\r\nflake8 noventis/\r\nblack noventis/\r\n```\r\n\r\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.\r\n\r\n---\r\n\r\n## \ud83d\udc65 Contributors\r\n\r\nThis project exists thanks to all the people who contribute:\r\n\r\n| Contributor | Role |\r\n| ------------------------- | ------------------- |\r\n| **Richard** | Product Manager |\r\n| **Fatoni Murfids** | AI Product Manager |\r\n| **Ahmad Nafi Mubarok** | Lead Data Scientist |\r\n| **Orie Abyan Maulana** | Lead Data Analyst |\r\n| **Grace Wahyuni** | Data Analyst |\r\n| **Alexander Angelo** | Data Scientist |\r\n| **Rimba Nevada** | Data Scientist |\r\n| **Jason Surya Winata** | Frontend Engineer |\r\n| **Nada Musyaffa Bilhaqi** | Product Designer |\r\n\r\n### Special Thanks\r\n\r\nA huge thank you to the maintainers of our dependencies:\r\n\r\n- pandas, numpy, scikit-learn, and the entire Python scientific computing community\r\n- XGBoost, LightGBM, and CatBoost teams for excellent gradient boosting libraries\r\n- Optuna and FLAML teams for amazing AutoML frameworks\r\n\r\n---\r\n\r\n## \ud83d\udcc2 Project Structure\r\n\r\nThe folder structure of **Noventis** project:\r\n\r\n```bash\r\n.\r\n\u251c\u2500\u2500 \ud83d\udcc1 dataset_for_examples/ # Sample datasets for testing\r\n\u251c\u2500\u2500 \ud83d\udcc1 docs/ # Documentation files\r\n\u251c\u2500\u2500 \ud83d\udcc1 examples/ # Example notebooks and scripts\r\n\u251c\u2500\u2500 \ud83d\udcc1 noventis/ # Main library code\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc1 __pycache__/\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc1 asset/ # Asset files (if any)\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc1 core/ # Core functionality\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc1 data_cleaner/ # Data cleaning module\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 __init__.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 auto.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 data_quality.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 encoding.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 imputing.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 orchestrator.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 outlier_handling.py\r\n\u2502 \u2502 \u2514\u2500\u2500 \ud83d\udcc4 scaling.py\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc1 eda_auto/ # EDA automation module\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 __init__.py\r\n\u2502 \u2502 \u2514\u2500\u2500 \ud83d\udcc4 eda_auto.py\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc1 predictor/ # Prediction module\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 __init__.py\r\n\u2502 \u2502 \u251c\u2500\u2500 \ud83d\udcc4 auto.py\r\n\u2502 \u2502 \u2514\u2500\u2500 \ud83d\udcc4 manual.py\r\n\u2502 \u2514\u2500\u2500 \ud83d\udcc4 __init__.py # Main package init\r\n\u251c\u2500\u2500 \ud83d\udcc1 noventis.egg-info/ # Package metadata\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc4 dependency_links.txt\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc4 PKG-INFO\r\n\u2502 \u251c\u2500\u2500 \ud83d\udcc4 SOURCES.txt\r\n\u2502 \u2514\u2500\u2500 \ud83d\udcc4 top_level.txt\r\n\u251c\u2500\u2500 \ud83d\udcc1 tests/ # Unit tests\r\n\u251c\u2500\u2500 \ud83d\udcc4 .gitignore # Git ignore rules\r\n\u251c\u2500\u2500 \ud83d\udcc4 LICENSE # MIT License\r\n\u251c\u2500\u2500 \ud83d\udcc4 MANIFEST.in # Package manifest\r\n\u251c\u2500\u2500 \ud83d\udcc4 pyproject.toml # Modern Python packaging config\r\n\u251c\u2500\u2500 \ud83d\udcc4 README.md # This file\r\n\u251c\u2500\u2500 \ud83d\udcc4 requirements.txt # Production dependencies\r\n\u251c\u2500\u2500 \ud83d\udcc4 requirements-dev.txt # Development dependencies\r\n\u2514\u2500\u2500 \ud83d\udcc4 setup.py # Package setup script\r\n```\r\n\r\n### \ud83d\udccc Notes\r\n\r\n- The `noventis/` folder contains the **main library code**\r\n- The `tests/` folder is dedicated to **unit testing and integration testing**\r\n- `setup.py` and `pyproject.toml` are used for **packaging and distribution**\r\n- `requirements.txt` lists the **external dependencies** needed for the project\r\n\r\n\ud83d\ude80 With this structure, the project is ready for development, testing, and publishing on **PyPI or GitHub**.\r\n\r\n---\r\n\r\n## \ud83d\udd27 Troubleshooting\r\n\r\n### Common Issues\r\n\r\n**Problem**: `ModuleNotFoundError: No module named 'noventis'`\r\n\r\n```bash\r\n# Solution: Reinstall the package\r\npip uninstall noventis\r\npip install noventis\r\n```\r\n\r\n**Problem**: Dependencies conflict\r\n\r\n```bash\r\n# Solution: Create a fresh virtual environment\r\npython -m venv fresh_env\r\nsource fresh_env/bin/activate\r\npip install noventis\r\n```\r\n\r\n**Problem**: Import errors after installation\r\n\r\n```python\r\n# Solution: Verify installation\r\nimport noventis\r\nprint(noventis.__version__)\r\nnoventis.print_info() # Check all dependencies\r\n```\r\n\r\n### Getting Help\r\n\r\n- \ud83d\udcd6 [Documentation](https://docs.noventis.dev)\r\n- \ud83d\udc1b [GitHub Issues](https://github.com/bcc/noventis/issues)\r\n\r\n---\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n### Third-Party Licenses\r\n\r\nNoventis uses several open-source libraries. We are grateful to their maintainers:\r\n\r\n- **Data Processing**: pandas (BSD), numpy (BSD), scipy (BSD)\r\n- **Visualization**: matplotlib (PSF), seaborn (BSD)\r\n- **Machine Learning**: scikit-learn (BSD), xgboost (Apache 2.0), lightgbm (MIT), catboost (Apache 2.0)\r\n- **AutoML**: optuna (MIT), flaml (MIT), shap (MIT)\r\n- **Feature Engineering**: category_encoders (BSD), statsmodels (BSD)\r\n\r\nAll dependencies are licensed under permissive open-source licenses (BSD, MIT, Apache 2.0).\r\n\r\n---\r\n\r\n## \ud83d\udcda Citation\r\n\r\nIf you use Noventis in your research, please cite:\r\n\r\n```bibtex\r\n@software{noventis2025,\r\n author = {Noventis Team},\r\n title = {Noventis: Intelligent Automation for Data Analysis},\r\n year = {2025},\r\n url = {https://github.com/bccfilkom/noventis}\r\n}\r\n```\r\n\r\n---\r\n\r\n## \ud83c\udf1f Star History\r\n\r\n[](https://star-history.com/#yourusername/noventis&Date)\r\n\r\n---\r\n\r\n<div align=\"center\">\r\n\r\nMade with \u2764\ufe0f by [Noventis Team](https://noventis.dev)\r\n\r\nIf you find Noventis useful, please consider giving it a \u2b50 on [GitHub](https://github.com/yourusername/noventis)!\r\n",
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