<p align="center">
<img src="https://svgshare.com/i/wCo.svg" alt="wizcraft-banner" />
</p>
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/wiz-craft)
# 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)**
## 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)
## 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.
<p align="center">
<img src="https://i.imgur.com/jYLwMN7.png" alt="wizcraft-cli_welcome" width = "600" height = "300" />
</p>
## Features Available
### Data Description
<p>
<img src="https://i.imgur.com/2CUMMoX.png" alt="data_description_preview" />
</p>
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
<p>
<img src="https://i.imgur.com/JlkyQl5.png" alt="null_data_preview" />
</p>
1. Show NULL value counts in each column.
2. Remove specific columns or fill NULL values with mean, median, or mode, or even using KNN technique.
### Encode Categorical Values
<p>
<img src="https://i.imgur.com/0gEfhpi.png" alt="one_hot_encode_preview" />
</p>
1. Identify and list categorical columns.
2. Perform one-hot encoding on categorical columns.
### Feature Scaling
<p>
<img src="https://i.imgur.com/kfpoXeG.png" alt="scaling_preview" />
</p>
1. Normalize the data in a column using Min-Max scaling or Standard Scaler.
### Save Preprocessed Dataset
<p>
<img src="https://i.imgur.com/1XywkGQ.png" alt="save_preview" />
</p>
1. Download the modified dataset with applied preprocessing steps.
Raw data
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