Metadata-Version: 2.1
Name: da_viz
Version: 0.1
Summary: A package for visualizing data and machine learning model performance
Home-page: https://github.com/mixter3011/da_vis.git
Author: Sabyasachi
Author-email: sabychakraborty08@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENCE
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Requires-Dist: shap
Requires-Dist: pandas
Requires-Dist: dash
Requires-Dist: plotly

# da_vis

`da_vis` is a Python package for visualizing data and machine learning model performance. It provides tools for generating various types of plots and dashboards to analyze and present data insights.

## Installation

You can install `da_vis` using pip:

```bash
pip install da-vis
```
## Usage

```python
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from da_vis.visualizer import DataVisualizer

data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)

model = RandomForestClassifier()
model.fit(df, data.target)

visualizer = DataVisualizer(data=df, model=model)

visualizer.correlation_heatmap()
visualizer.feature_distribution('sepal length (cm)')
visualizer.confusion_matrix(data.target, model.predict(df))
visualizer.roc_curve(data.target, model.predict_proba(df))
visualizer.feature_importance()
visualizer.tsne_plot()
```
## Features

- **Correlation Heatmap**: Visualizes the correlation matrix of a dataset.
- **Feature Distribution**: Plots the distribution of a specific feature.
- **Confusion Matrix**: Displays the confusion matrix for model evaluation.
- **ROC Curve**: Generates the ROC curve for binary classification models.
- **Feature Importance**: Shows feature importance scores for the model.
- **t-SNE Plot**: Creates a t-SNE plot for visualizing high-dimensional data.

## Contributing

Contributions are welcome! Feel free to submit bug reports, feature requests, or pull requests through GitHub issues and pull requests.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
