Project Overview

A GUI application to perform tasks such as data cleaning, data engineering and machine learning without writing any code. It is a data prototyping tool for students and researchers to do prelimnary analysis of their dataset.

Features

• Data Imports in CSV, JSON, XLSX and HTML format.
• Null Values imputation with Fill Forward, Fill Backward, Fill Median, Fill Mean and Drop.
• Preprocessors like Label Encoding, Normalizer and Standardizer.
• Horizontal and Vertical Joins of Dataset
• Visualizations like Line Chart, Bar Chart, Pie Chart and Histogram.
• View dataset as a table.
• Regression Algorithms supported: Linear Regression, Support Vector Machine (SVM), Decision Trees, Random Forest and K-Nearest Neighbors (KNN).
• Classification Algorithms supported: Logistic Regression, Support Vector Machine (SVM), Decision Trees, Random Forest and K-Nearest Neighbors (KNN).
• Data Exports in CSV, JSON, XLSX and HTML format.

Screenshots

TechStack

• Python
• Django
• Dash
• Plot.ly
• Pandas
• Scikit-learn
• Bootstrap
• HTML
• CSS
• JS

Diagrams

Class Diagram

Visulalization using Django Dash

Contributors

• Avnish Pal
• Vishnu P
• Nitesh Nanda
• Varun Rawat
• Harsh Chamoli

Setup

NOTE: Use powershell on Windows for this setup.

1. Install Docker
2. Change directory for datawiz.

cd datawiz/

3. Add executable permissions for executor.sh.

chmod +x ./executor.sh

4. Execute

./executor.sh

License

MIT