Data Clean

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Problem

In every machine learning workflow, data cleaning is one of the most time-consuming and repetitive tasks. Yet, as ML engineers, our true focus should be on building models, crafting architectures, and solving real problems - not spending endless hours handling missing values, formatting inconsistencies and unwanted noise in CSV files.

Solution

That's exactly why I build this CSV Data Cleaning App. This tool helps you clean your data in few steps. All you need to do is to click on the button the operation you want to apply on the file. After applying all the operations, you can download the final cleaned CSV File.

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💻 Tech Stack:

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Clean Data Dashboard

An interactive web-based tool built with Gradio for data cleaning, preprocessing, and profiling.
It helps ML engineers and data scientists clean messy datasets quickly — handling missing values, encoding, outliers, and generating detailed reports.

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🚀 Features

✅ Upload and preview CSV datasets
✅ Delete, rename, normalize columns
✅ Handle missing values (Mean, Median, Mode)
✅ Detect and remove duplicates
✅ Apply Label / Ordinal encoding
✅ Change column data types
✅ Outlier detection via IQR, Z-score, Winsorization, Min-Max
✅ Generate detailed pandas-profiling reports
✅ Download cleaned datasets

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Project Structure

• .devcontainer
• .github/workflows/code-check.yaml
• tests
• helper_functions.py
• report_generation.py
• app.py
• requirements.txt
• README.md
• LICENSE

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How to run locally

Clone the repo

git clone https://github.com/NNEngine/Data-Clean.git

Then, run

pyhthon app.py

Then, upload the CSV File

And Perform the Cleaning Operations Listed Above

Reort Generation

After CLeaning the Data, You can generate the detailed interactive report of the data