Demo Project

Data Cleaning Automation

End-to-end cleaning pipeline that detects and fixes 10 types of data quality issues on a deliberately messy CRM export with 150 records.

Issues Found and Fixed

31
Total Issues Fixed
150
Records Cleaned
18
Bad Dates Fixed
13
Neg Spend Fixed

Static Output

Data Cleaning Report chart

Left: Issues detected across categories. Right: Customer distribution by city after cleaning and standardization.

Interactive Chart Try It

Hover to see exact values. City distribution shown as an interactive pie chart.

Open interactive chart full screen

Issues Detected and Resolved

Notebook

Follow the 10-step cleaning process in Jupyter Notebook.

Download .ipynb Notebook

Tech Stack

Python Pandas NumPy Matplotlib Data Quality Plotly
View Repository Download Script Architecture Getting Started Interactive Chart