🧠 Data Quality Dashboard

A Full-Stack + Data Analysis project designed to monitor, visualize, and improve dataset integrity in real-time.
Built with Python, Pandas, Matplotlib, and an interactive Streamlit frontend — this dashboard empowers data professionals to identify and resolve data quality issues quickly and efficiently.

⚙️ Note: The live backend (hosted on Railway) was part of a trial deployment and is currently inactive.
The application runs perfectly on localhost, where all backend and frontend features function as intended.

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🚀 Features

• 🧩 Automated Data Cleaning — Detects and handles missing values, duplicates, and inconsistencies.
• 📊 Error Rate Computation — Calculates error ratios and data completeness metrics.
• 🎨 Interactive Frontend (Streamlit) — Real-time charts, filters, and summaries for better visualization.
• 🔄 End-to-End Integration — Backend API (Flask) connected with Streamlit frontend for seamless operation.
• ☁️ Deployment Ready — Designed to run both locally and on cloud platforms like Railway or Render.

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⚙️ Tech Stack

Languages & Libraries:
🐍 Python, Pandas, NumPy, Matplotlib, Seaborn

Frontend:
🖥️ Streamlit

Backend:
🧠 Flask (API)

Database:
🍃 MongoDB

Deployment (Trial):
🚉 Railway (currently expired, can be re-deployed easily)

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🖥️ Run Locally

Clone the project:

git clone https://github.com/Iqbal-dev12/Data-Quality-Dashboard.git
cd Data-Quality-Dashboard