# celoMine
<div align="center">
<img src="./celoMine_logo.png" alt="Logo celoMine" width="50%">
</div>
[](
https://pypi.org/project/celoMine/)
[](
https://stackoverflow.com/questions/tagged/celomine)
[](
https://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/forthcoming-special-issues/artificial-intelligence-for-process-mining)
celoMine brings predictive machine learning to your Celonis analyses to enable deep process insights and data-driven decision-making.
This powerful python framework leverage machine learning algorithms and visualization techniques within your Celonis analyses to drive process optimization.
- **Source code:** https://github.com/JeanBertinR/celoMine
- **Bug reports:** https://github.com/JeanBertinR/celoMine/issues
- **Report a security vulnerability:** https://tidelift.com/docs/security
## Requirements
- Python 3.x
- pandas
- scikit-learn
- matplotlib
## Installation
You can install the `celoMine` package using `pip`. Here's the installation command from your terminal:
```shell
pip install celoMine
```
Make sure you have Python and pip installed on your system before running this command.
After the installation, you can import the package in your Python code using the following line:
```python
import celoMine
```
That's all it takes to install the package and import it into your project.
## Usage
### Analyzing Event Logs
The `AnalyseEventLog` class allows you to preprocess and analyze event log data. Here is an example of how to use it:
```python
from analyse_event_log import AnalyseEventLog
# Create an instance of AnalyseEventLog
log_analyzer = AnalyseEventLog()
# Load event log data from a CSV file
log_analyzer.load_data('event_log.csv')
# Preprocess the data
log_analyzer.preprocess_data()
# Train a machine learning model
log_analyzer.train_model()
# Get the accuracy of the model
accuracy = log_analyzer.get_model_accuracy()
# Visualize the event log data
log_analyzer.visualize_data()
```
### Visualizing Event Logs
The `VisualizeEventLog` class allows you to visualize event log data. Here is an example of how to use it:
```python
from visualize_event_log import VisualizeEventLog
# Create an instance of VisualizeEventLog
log_visualizer = VisualizeEventLog()
# Load event log data from a CSV file
log_visualizer.load_data('event_log.csv')
# Plot the frequency of events over time
log_visualizer.plot_event_frequency()
# Plot the distribution of events by category
log_visualizer.plot_event_category_distribution()
```
### Contributing
Contributions are welcome! If you have any suggestions or find any issues, please open an issue or submit a pull request.
### License
This project is licensed under the GPL v3 License.
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"description": "# celoMine\r\n<div align=\"center\">\r\n <img src=\"./celoMine_logo.png\" alt=\"Logo celoMine\" width=\"50%\">\r\n</div>\r\n\r\n[](\r\nhttps://pypi.org/project/celoMine/)\r\n[](\r\nhttps://stackoverflow.com/questions/tagged/celomine)\r\n[](\r\nhttps://www.journals.elsevier.com/engineering-applications-of-artificial-intelligence/forthcoming-special-issues/artificial-intelligence-for-process-mining)\r\n\r\n\r\nceloMine brings predictive machine learning to your Celonis analyses to enable deep process insights and data-driven decision-making.\r\nThis powerful python framework leverage machine learning algorithms and visualization techniques within your Celonis analyses to drive process optimization.\r\n\r\n- **Source code:** https://github.com/JeanBertinR/celoMine\r\n- **Bug reports:** https://github.com/JeanBertinR/celoMine/issues\r\n- **Report a security vulnerability:** https://tidelift.com/docs/security\r\n\r\n\r\n\r\n## Requirements\r\n\r\n- Python 3.x\r\n- pandas\r\n- scikit-learn\r\n- matplotlib\r\n\r\n## Installation\r\n\r\nYou can install the `celoMine` package using `pip`. Here's the installation command from your terminal:\r\n\r\n```shell\r\npip install celoMine\r\n```\r\nMake sure you have Python and pip installed on your system before running this command.\r\nAfter the installation, you can import the package in your Python code using the following line:\r\n```python\r\nimport celoMine\r\n```\r\nThat's all it takes to install the package and import it into your project.\r\n\r\n## Usage\r\n\r\n### Analyzing Event Logs\r\n\r\nThe `AnalyseEventLog` class allows you to preprocess and analyze event log data. Here is an example of how to use it:\r\n\r\n```python\r\nfrom analyse_event_log import AnalyseEventLog\r\n\r\n# Create an instance of AnalyseEventLog\r\nlog_analyzer = AnalyseEventLog()\r\n\r\n# Load event log data from a CSV file\r\nlog_analyzer.load_data('event_log.csv')\r\n\r\n# Preprocess the data\r\nlog_analyzer.preprocess_data()\r\n\r\n# Train a machine learning model\r\nlog_analyzer.train_model()\r\n\r\n# Get the accuracy of the model\r\naccuracy = log_analyzer.get_model_accuracy()\r\n\r\n# Visualize the event log data\r\nlog_analyzer.visualize_data()\r\n```\r\n\r\n### Visualizing Event Logs\r\nThe `VisualizeEventLog` class allows you to visualize event log data. Here is an example of how to use it:\r\n\r\n```python\r\nfrom visualize_event_log import VisualizeEventLog\r\n\r\n# Create an instance of VisualizeEventLog\r\nlog_visualizer = VisualizeEventLog()\r\n\r\n# Load event log data from a CSV file\r\nlog_visualizer.load_data('event_log.csv')\r\n\r\n# Plot the frequency of events over time\r\nlog_visualizer.plot_event_frequency()\r\n\r\n# Plot the distribution of events by category\r\nlog_visualizer.plot_event_category_distribution()\r\n```\r\n\r\n### Contributing\r\nContributions are welcome! If you have any suggestions or find any issues, please open an issue or submit a pull request.\r\n\r\n### License\r\nThis project is licensed under the GPL v3 License.\r\n",
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