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https://github.com/wiz-craft/wiz-craft

A CLI-based dataset preprocessing tool for machine learning tasks. Features include data exploration, null value handling, one-hot encoding, and feature scaling, and download the modified dataset effortlessly.
https://github.com/wiz-craft/wiz-craft

cli cli-app dataset machine-learning preprocessing

Last synced: about 2 months ago
JSON representation

Host: GitHub
URL: https://github.com/wiz-craft/wiz-craft
Owner: wiz-craft
License: mit
Created: 2024-04-17T04:16:29.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-04-17T04:16:38.000Z (9 months ago)
Last Synced: 2024-08-08T00:43:07.876Z (5 months ago)
Topics: cli, cli-app, dataset, machine-learning, preprocessing
Language: Python
Homepage: https://pypi.org/project/wiz-craft/
Size: 72.3 KB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

project-awesome - wiz-craft/wiz-craft - A CLI-based dataset preprocessing tool for machine learning tasks. Features include data exploration, null value handling, one-hot encoding, and feature scaling, and download the modified dataset effo (Python)

README

        


  

  





  



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# WizCraft - CLI-Based Dataset Preprocessing Tool

WizCraft is a cutting-edge Command Line Interface (CLI) tool developed to simplify the process of dataset preprocessing for machine learning tasks. It aims to provide a seamless and efficient experience for data scientists of all levels, facilitating the preparation of data for various machine-learning applications.

**[Try the tool online here](https://replit.com/@PinakDatta/DataWiz)**

**Check out the [Contribution Guide](https://github.com/Pinak-Datta/wiz-craft/blob/main/CONTRIBUTING.md) if you want to Contribute to this project**

## Table of Contents

- [Features](#features)

- [Getting Started](#getting-started)

  - [Installation](#installation)

- [Tasks](#tasks)

  - [Data Description](#data-description)

  - [Handle Null Values](#handle-null-values)

  - [Encode Categorical Values](#encode-categorical-values)

  - [Feature Scaling](#feature-scaling)

  - [Save Preprocessed Dataset](#save-preprocessed-dataset)

- [Future Works](#future-works)

- [Contributing to the Project](#contribute-to-the-project)

## Features

- Load and preprocess your dataset effortlessly through a Command Line Interface (CLI).

- View dataset statistics, null value counts, and perform data imputation.

- Encode categorical variables using one-hot encoding.

- Normalize and standardize numerical features for better model performance.

- Download the preprocessed dataset with your desired modifications.

## Getting Started

### Installation

1. Run the pip command:

   ```bash

   pip install wiz-craft

2. To use the module, use the commands:

    ```python

    from wizcraft.preprocess import Preprocess

    wiz_obj = Preprocess()

    wiz_obj.start()  

3. Follow the on-screen prompts to load your dataset, select target variables, and perform preprocessing tasks.



  



## Features Available

### Data Description



  



1. View statistics and properties of numeric columns.

2. Explore unique values and statistics of categorical columns.

3. Display a snapshot of the dataset.

### Handle Null Values



  



1. Show NULL value counts in each column.

2. Remove specific columns or fill NULL values with mean, median, or mode.

### Encode Categorical Values



  



1. Identify and list categorical columns.

2. Perform one-hot encoding on categorical columns.

### Feature Scaling



  



1. Normalize (Min-Max scaling) or standardize (Standard Scaler) numerical columns.

### Save Preprocessed Dataset



  



1. Download the modified dataset with applied preprocessing steps.

## Future Works

- [x] Advanced Data Imputation Techniques: Adding support for advanced data imputation techniques, such as K-nearest neighbours (KNN) imputation.

- [x] Improved UI and UX using Rich 

- [ ] Undo/Redo Option for each step

- [ ] Extension for NLP tasks (like tokenization, stemming)

- [ ] User-Friendly Interface: Improving the user interface to provide more interactive and user-friendly features, such as progress bars, error handling, and clear instructions.

- [ ] Using Curses for terminal Manipulation.

## Contributing to the Project

**Check out the [Contribution Guide](https://github.com/Pinak-Datta/wiz-craft/blob/main/CONTRIBUTING.md) if you want to contribute to this project**