wiz-craft 1.2.1

A beginner-friendly CLI for preprocessing tabular ML datasets with replayable recipes.

WizCraft - CLI-Based Dataset Preprocessing Tool

WizCraft is a beginner-friendly Command Line Interface (CLI) tool for preparing tabular datasets for machine learning. It helps you inspect a CSV, handle missing values, encode categorical columns, scale numeric features, save a cleaned dataset, and export replayable preprocessing recipes.

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
• Replayable Recipes
• Roadmap
• 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.
• Save preprocessing recipes and replay them on future CSV files.

Getting Started

Installation

Install WizCraft from PyPI:

pip install wiz-craft

Start the interactive CLI with a CSV file:

wizcraft dataset.csv

You can also launch WizCraft and choose a CSV from the current directory:

wizcraft

WizCraft can still be used from Python:

from wizcraft.preprocess import Preprocess

wiz_obj = Preprocess(csv_file="dataset.csv")
wiz_obj.start()

Follow the on-screen prompts to select the target variable and perform preprocessing tasks.

Replay a saved recipe on another CSV:

wizcraft apply new-data.csv --recipe cleaned.recipe.json --out new-data-clean.csv

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, mode, or K-nearest neighbors.

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.
2. Save a replayable .recipe.json file for the same preprocessing flow.

Replayable Recipes

WizCraft can now save the preprocessing steps you perform interactively. A recipe is a small JSON file that can be applied again later:

wizcraft apply raw-data.csv --recipe cleaned.recipe.json --out cleaned-data.csv

Recipes currently support:

• Removing columns
• Filling null values with mean, median, mode, or K-nearest neighbors
• One-hot encoding categorical columns
• Normalizing or standardizing numeric columns

Roadmap

WizCraft is being rebuilt around two ideas: a friendly first-time CLI and repeatable preprocessing recipes.

Current priorities:

• Non-interactive commands for automation and notebooks.
• Dataset health reports for nulls, duplicates, types, cardinality, and target balance.
• Exportable scikit-learn preprocessing pipelines.
• Cleaner terminal tables, validation, and error messages.
• Example datasets, tutorials, and good first issues for new contributors.

See ROADMAP.md for the full direction.

Contributing to the Project

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