A Streamlit web app that automatically detects dataset type (like Employee, Car, or Student data) and normalizes numeric columns using Python, Pandas, and Scikit-Learn — all offline and free.
💡 Originally designed to work with OpenAI / Google Gemini APIs, but now optimized for offline use — no API key or internet required!
-
📤 Upload CSV or Excel files
Supports.csvand.xlsxformats up to 200MB. -
🧠 Automatic Dataset Type Detection (Offline)
The app smartly detects dataset type (Employee, Student, Car, etc.) using column names. -
⚙️ Data Normalization
- Min-Max Normalization
- Standard (Z-Score) Normalization
-
📊 Live Data Preview
View your original and normalized data instantly. -
📥 Download Normalized File
Export normalized data in both CSV and Excel formats. -
💾 Offline & Free
100% local — no API calls, no cost, no rate limits.
| Technology | Purpose |
|---|---|
| 🐍 Python 3.10+ | Core programming language |
| 📊 Pandas | Data handling & cleaning |
| 🧮 Scikit-Learn | Data normalization (MinMaxScaler, StandardScaler) |
| 📘 OpenPyXL | Excel file support |
| 🌐 Streamlit | Interactive web UI |
| 🧠 (Optional) Gemini / OpenAI API | For future AI dataset detection |
git clone https://github.com/ZohaiAli/offline-data-normalizer.git
cd offline-data-normalizer