https://github.com/alienobserver/datengeist

Application for easy understanding of unstructured data
https://github.com/alienobserver/datengeist

feature-engineering machine-learning streamlit

Last synced: about 2 months ago
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Application for easy understanding of unstructured data

• Host: GitHub
• URL: https://github.com/alienobserver/datengeist
• Owner: alienobserver
• License: apache-2.0
• Created: 2024-11-28T11:29:00.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-28T12:17:31.000Z (2 months ago)
• Last Synced: 2024-11-28T12:28:11.702Z (2 months ago)
• Topics: feature-engineering, machine-learning, streamlit
• Language: Python
• Homepage:
• Size: 0 Bytes
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Datengeist

## Application for easy understanding of unstructured data

Datengeist is a streamlit built application which is made to understand unstructured data through visualization 

of its components. Datengeist is working with **.csv** files. Datengeist has this key functionalities:

1. Categorization of features

2. Visualization of distributions

3. Convenient handling of missing data

4. Tools for feature comparison

To run datengeist you can install via pip

```

$ pip install datengeist

$ datengeist start

```

Or you can create a virtual environment and then run it (recommended)

```

$ python3 -m venv datengeist_env

$ source datengeist_env/bin/activate

$ pip install datengeist

```

### 1. Sample the Dataset

Sample the Dataset is where you can sample data, load it and have your first overview of the data



### 2. General Info

General Info is where you can divide your features into corresponding categories and view your

missing values in each feature



### 3. Feature Info

Feature Info is where you can view your features more closely, the distributions and missing value percentage





### 4. Relate Features

Relate Features is where you can view the correlation between your features and relate them via box plotting



### License 

Apache 2.0