Wiz-craft

What's Changed
* Completely revamped and a new look added to the tool, improving the UI/UX. Basically a new file `io.py` was added that used the `rich` library in Python to colourize the terminal, and we use the `io` file in every operation we perform. A glimpse of what the terminal looks like right now:
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<img src = "https://i.imgur.com/2CUMMoX.png" >
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* An error was reported that the changes were not reflected in the modified new dataset when saving it after any operations were performed. ( Surprisingly this worked fine if we tried to save the dataset after only performing the `Remove Null Values` operation.) . Anyway, that is fixed now!

* Now, a new option is introduced to handle Null values in the dataset! You can use the concept of K-Nearest Neighbours (KNN), to fill up the empty cells in the columns.


New Contributors:
* Special thanks to the new contributors newtoallofthis123, parth-verma7 and pranavpathak08 for their contributions that helped in incorporating the new features to this tool. Looking forward to more of their contribution. 😄

What's Changed
* Containerized Codes under a common File (for better reference after import).
Importing now looks like this:
python
from wizcraft.preprocess import Preprocess

tool = Preprocess()
tool.start()

* Changes in PyPi:
* MIT License added
* GitHub Stats Linked

Bug Fixes
* The pre-existing error of `no such module found`, faced while importing, is fixed.

beta
Description:

WizCraft 1.0.1 marks the debut release of our powerful CLI-Based Dataset Preprocessing Tool. With a focus on streamlining data preparation for machine learning tasks, this release introduces a range of essential functionalities to enhance data quality and facilitate efficient preprocessing.

Key Features:

Data Description:

Understand your dataset better with insightful statistics and properties of numeric and categorical columns.

Handle Null Values:

Identify and handle null values effortlessly by removing specific columns or filling them with mean, median, or mode.

Encode Categorical Values:

One-hot encoding of categorical columns to convert them into numerical representations for improved model performance.

Feature Scaling:

Normalize and standardize numerical features to ensure consistency and eliminate scale bias.

Save Preprocessed Dataset:

Download the modified dataset with applied preprocessing steps to seamlessly integrate with machine learning pipelines.

What's Next:

The first release of WizCraft sets the foundation for a versatile and user-friendly dataset preprocessing experience. In upcoming updates, we plan to introduce more advanced features, such as an undo facility and automatic terminal clearing after each operation, to enhance user convenience and streamline the preprocessing workflow.

Stay tuned for further updates as we continue to enhance the capabilities of WizCraft, making it an essential tool in every data scientist's toolkit.

Note: The availability of the release on PyPI may vary, so make sure to check for updates and installation instructions on our official repository.