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

Check out the Contribution Guide if you want to Contribute to this project

Table of Contents

• Features
• Getting Started
  • Installation
• Tasks
  • Data Description
  • Handle Null Values
  • Encode Categorical Values
  • Feature Scaling
  • Save Preprocessed Dataset
• Future Works
• Contributing 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:

   pip install wiz-craft
   

2. To use the module, use the commands:

   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

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

• 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 if you want to contribute to this project