# Data-Help
Data-Help is a Python library designed to assist data science and data analysis teams in their workflow. It provides various utility functions and tools to streamline common data science tasks.
## Features
Data-Help offers the following key features:
1. **Project Management:** Data-Help simplifies the creation of standard data science project structures. With a single function call, you can generate a well-organized project directory with predefined folders for datasets, processed data, raw data, outputs, models, scripts, notebooks, and more.
2. **Model Saving and Loading:** Data-Help provides easy-to-use functions for saving and loading trained machine learning models. It supports various formats such as joblib, pickle, and keras, enabling seamless integration with different model types.
3. **Data Exploration and Visualization:** The library includes functions for data exploration, summary statistics, and visualization. You can quickly generate feature importances plots and visualize missing data to gain insights into your datasets.
4. **Feature Engineering:** Data-Help includes methods for handling missing data and noise in your datasets. It offers functions for dropping missing columns based on a specified threshold and detecting outliers using Tukey's Interquartile Range (IQR) method.
5. **Model Evaluation and Cross-Validation:** Data-Help provides tools to evaluate model performance, including functions to calculate accuracy, F1-score, precision, recall, and generate classification reports. It also supports cross-validation for model evaluation.
6. **Scaling and Normalization:** The library offers functions for min-max scaling and z-score normalization of data to bring features to a common scale.
## Getting Started
To use Data-Help in your data science projects, you can install it via pip:
```bash
pip install data-help
```
Once installed, you can import the library and explore its functionality:
```python
import data_help as dh
# Create a new data science project structure
dh.create_project("my_project")
# Load and preprocess data
df = dh.load_dataset("data/iris.csv")
X, y = dh.split_dataset(df)
# Train a machine learning model and get accuracy
trained_model, accuracy = dh.train_model(X, y)
print(f"Model Accuracy: {accuracy:.2f}")
# Plot feature importances
dh.plot_feature_importance(trained_model.feature_importances_, X.columns)
```
## Documentation
For detailed usage instructions and API reference, please refer to the official documentation at [https://data-help-docs.example.com](https://data-help-docs.example.com)
## Contribution
Data-Help is an open-source project, and we welcome contributions from the data science community. If you find a bug, have a feature request, or want to contribute improvements, please open an issue or submit a pull request on our GitHub repository at [https://github.com/data-help/data-help](https://github.com/data-help/data-help).
## License
Data-Help is licensed under the MIT License. See the [LICENSE](https://github.com/data-help/data-help/blob/main/LICENSE) file for more details.
## Contact
If you have any questions or feedback, feel free to reach out to our support team at dev.kitonga@gmail.com or join our community forum at [https://community.data-help.com](https://community.data-help.com). We are here to assist you in making your data science journey smooth and successful!
Raw data
{
"_id": null,
"home_page": "https://github.com/data_help",
"name": "data-help",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "",
"author": "Meshack Kitonga",
"author_email": "dev.kitonga@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/15/05/b8ccece698306f0e9818c0187779717d4d05e33ee5e53f58416d66a1191c/data-help-0.1.3.tar.gz",
"platform": null,
"description": "# Data-Help\n\nData-Help is a Python library designed to assist data science and data analysis teams in their workflow. It provides various utility functions and tools to streamline common data science tasks.\n\n## Features\n\nData-Help offers the following key features:\n\n1. **Project Management:** Data-Help simplifies the creation of standard data science project structures. With a single function call, you can generate a well-organized project directory with predefined folders for datasets, processed data, raw data, outputs, models, scripts, notebooks, and more.\n\n2. **Model Saving and Loading:** Data-Help provides easy-to-use functions for saving and loading trained machine learning models. It supports various formats such as joblib, pickle, and keras, enabling seamless integration with different model types.\n\n3. **Data Exploration and Visualization:** The library includes functions for data exploration, summary statistics, and visualization. You can quickly generate feature importances plots and visualize missing data to gain insights into your datasets.\n\n4. **Feature Engineering:** Data-Help includes methods for handling missing data and noise in your datasets. It offers functions for dropping missing columns based on a specified threshold and detecting outliers using Tukey's Interquartile Range (IQR) method.\n\n5. **Model Evaluation and Cross-Validation:** Data-Help provides tools to evaluate model performance, including functions to calculate accuracy, F1-score, precision, recall, and generate classification reports. It also supports cross-validation for model evaluation.\n\n6. **Scaling and Normalization:** The library offers functions for min-max scaling and z-score normalization of data to bring features to a common scale.\n\n## Getting Started\n\nTo use Data-Help in your data science projects, you can install it via pip:\n\n```bash\npip install data-help\n```\n\nOnce installed, you can import the library and explore its functionality:\n\n```python\nimport data_help as dh\n\n# Create a new data science project structure\ndh.create_project(\"my_project\")\n\n# Load and preprocess data\ndf = dh.load_dataset(\"data/iris.csv\")\nX, y = dh.split_dataset(df)\n\n# Train a machine learning model and get accuracy\ntrained_model, accuracy = dh.train_model(X, y)\nprint(f\"Model Accuracy: {accuracy:.2f}\")\n\n# Plot feature importances\ndh.plot_feature_importance(trained_model.feature_importances_, X.columns)\n```\n\n## Documentation\n\nFor detailed usage instructions and API reference, please refer to the official documentation at [https://data-help-docs.example.com](https://data-help-docs.example.com)\n\n## Contribution\n\nData-Help is an open-source project, and we welcome contributions from the data science community. If you find a bug, have a feature request, or want to contribute improvements, please open an issue or submit a pull request on our GitHub repository at [https://github.com/data-help/data-help](https://github.com/data-help/data-help).\n\n## License\n\nData-Help is licensed under the MIT License. See the [LICENSE](https://github.com/data-help/data-help/blob/main/LICENSE) file for more details.\n\n## Contact\n\nIf you have any questions or feedback, feel free to reach out to our support team at dev.kitonga@gmail.com or join our community forum at [https://community.data-help.com](https://community.data-help.com). We are here to assist you in making your data science journey smooth and successful!\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A python library for easy modelling",
"version": "0.1.3",
"project_urls": {
"Homepage": "https://github.com/data_help"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "94552e4fdcfb40660ec4517228869da8bb6971dcb900ee67433d1d14a2e1e2d1",
"md5": "cfbdc43e9a7af6b736cfdffd23eb17e0",
"sha256": "f9cdff9e60110537889d2893634a57d8ed6a1bdbde09de0896ceb88ee8ebf90e"
},
"downloads": -1,
"filename": "data_help-0.1.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "cfbdc43e9a7af6b736cfdffd23eb17e0",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 12637,
"upload_time": "2023-07-23T00:05:54",
"upload_time_iso_8601": "2023-07-23T00:05:54.469597Z",
"url": "https://files.pythonhosted.org/packages/94/55/2e4fdcfb40660ec4517228869da8bb6971dcb900ee67433d1d14a2e1e2d1/data_help-0.1.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "1505b8ccece698306f0e9818c0187779717d4d05e33ee5e53f58416d66a1191c",
"md5": "4b7afdffb9e9766040bd424c5b3a2f02",
"sha256": "05e0beffdf871b4fae752ba1e2d020fe24d9ff88c01647c79131e82d0e438a3c"
},
"downloads": -1,
"filename": "data-help-0.1.3.tar.gz",
"has_sig": false,
"md5_digest": "4b7afdffb9e9766040bd424c5b3a2f02",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 11963,
"upload_time": "2023-07-23T00:05:57",
"upload_time_iso_8601": "2023-07-23T00:05:57.404917Z",
"url": "https://files.pythonhosted.org/packages/15/05/b8ccece698306f0e9818c0187779717d4d05e33ee5e53f58416d66a1191c/data-help-0.1.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-07-23 00:05:57",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "data-help"
}
JSON