Metadata-Version: 2.1
Name: AI-Aquatica
Version: 0.1.0
Summary: The project is used to analyze water quality data using AI/ML tools.
Home-page: https://github.com/TyMill/AI-Aquatica
Author: Tymoteusz Miller
Author-email: me@tymoteuszmiller.dev
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: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: jinja2
Requires-Dist: sqlalchemy
Requires-Dist: pymongo
Requires-Dist: tensorflow
Requires-Dist: plotly

# AI-Aquatica

AI-Aquatica is a comprehensive Python library designed to analyze water quality data using advanced AI/ML tools. This library facilitates the processing, analysis, and visualization of water quality indicators, helping researchers and professionals make informed decisions based on their data.

## Features

- **Data Import**: Seamlessly import water quality data from various formats including CSV, Excel, JSON, SQL, and NoSQL databases.
- **Data Cleaning**: Efficiently clean your dataset by removing duplicates and handling missing values using various strategies.
- **Data Standardization**: Normalize and standardize your data for consistent analysis, including log, square root, and Box-Cox transformations.
- **Handling Missing Data**: Impute missing values using statistical methods or advanced AI/ML techniques like KNN, regression, and autoencoders.
- **Ion Balance Calculations**: Perform ion balance calculations to verify data integrity and identify potential errors.
- **Statistical Analysis**: Conduct basic and advanced statistical analyses, including correlation, ANOVA, and time series decomposition.
- **AI/ML Analysis**: Utilize machine learning models for regression, classification, clustering, anomaly detection, and data generation.
- **Data Visualization**: Create informative visualizations such as line plots, bar charts, scatter plots, heatmaps, PCA, t-SNE, and interactive bubble charts.
- **Report Generation**: Automatically generate comprehensive reports with statistical summaries and AI/ML analysis results.

## Installation

### Prerequisites

- Python 3.8 or higher
- pip (Python package installer)

## Contributing

Contributions are welcome! Please see the contributing guidelines for more details.

## License

This project is licensed under the MIT License.

## Acknowledgments

Special thanks to all contributors and the open-source community for their support and contributions.
