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This sweet little program is to data-set as your soap is to your body. The end result will be clean, shiny, more beautiful. Check it out.

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QuickWash

QuickWash is a sibling program from the series of program I have been preparing for some time:-

This program has been specifically designed to clean any dataset quickly and efficiently without demanding the user to enter any codes.

The key points to know about this program are as follows:-

System requirements:-

1: Python program along with modules pandas and sklearn installed.

2: Maybe a Windows PC because I don't know how the programming language interacts with other OS.

Features supported:-

1: Checking if any dataset conatin missing values.

2: Separating cleaning operations between categorical and numerical data in dataset.

3: Remove unwanted columns.

4: Remove rows with missing values.

5: Choosing mean, median, mode any other types of ways to fill missing values.

6: Can perform cleaning operation on the whole dataset at once or column by column.

7: Fill missing values with your own data.

8: Fill each cell with missing value.

9: Verifier to check if the data entered by user is null or valid.

10: Simple Imputer module from sklearn for quick imputing.

11: Smart sensing mechanism to make sure the user enters only valid data.

12: No coding required while using.

13: Completely works in terminal/command prompt without error.

14: Completely flexible so you can code and make a few changes as your need.

15: Works in a wizard format so, the problem will appear before you instead of you searching for it.

Future plan:-

1: Cuurently I am learning software designing to turn this program into a proper software with a GUI.

2: Adding more useful features.

3: Split the program in smaller parts to be used with other programs as well.

4: Removing bugs if any. I don't know if there are any.

Hope, you will like it.

Thank you.

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This sweet little program is to data-set as your soap is to your body. The end result will be clean, shiny, more beautiful. Check it out.

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