data-help 0.1.3
A python library for easy modelling
License: MIT
Requires Python: >=3.6

# 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!