Welcome to the DQW Structured data repository! 🏗️

This repo contains the structured data DQW streamlit app code, however, the streamlit apps have been split into 5 for maintenance purposes:

• Main Streamlit app 📊
• Tabular Data Section 🏗️
• Audio Data Section 🎶
• Text Data Section 📚
• Image Data Section 🖼️

The packages used in the application are in the table below.

App section	Description	Visualisation	Selection	Package
Synthetic tabular	x	x		table-evaluator
Tabular	x	x		sweetviz
Tabular	x	x		pandas-profiling
Tabular, text			x	PyCaret

Structured (tabular) data

Key points addressed:

• Quantitative measures – number of rows and columns.
• Qualitative measures – column types.
• Descriptive statistics with NumPy for numeric columns, for example, count, mean, percentiles and standard deviation. For discrete columns, count, unique, top and frequency.
• Explore missing data.
• Examine outliers.
• Mitigate class imbalance.
• Compare datasets, like train, test and evaluate data.
• Evaluate synthetic datasets.
• Create a quality report.

To complete the key points, 4 subsections are created:

• One file EDA with pandas-profiling
• One file preporcessing with PyCaret
• Two file comparison with Sweetviz
• Synthetic data evaluation with table-evaluator
• In all the sections, there is an option to download a pdf/zip of the results

How to run locally

1. Installation process:

   Create virtual environment and activate it - https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/

   Clone or download files from this repo

   Run pip install -r requirements.txt

   Run streamlit app.py to launch app

2. Software dependencies:

   In requirements.txt

3. Latest releases

   Use app.py