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:

pip install da-viz==0.1

Usage

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