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Nevora: Transform raw sales and returns data into Nevada chart format for warranty and reliability analysis.

Project description

# 📦 Nevora
---

# Nevora: Transform Warranty and Reliability Data with Ease

Nevora helps you seamlessly convert sales and returns data into Nevada chart format — the essential starting point for warranty analysis, survival modeling, and reliability forecasting.  
Built for engineers, analysts, and data scientists who want clean, analysis-ready data without the manual grind.

---
[pyproject.toml](pyproject.toml)
## 🚀 Key Features
- 📊 Nevada Chart Converter: Instantly pivot raw sales/returns into a professional Nevada matrix.
- ⏳ Auto Time Bucketing: Monthly, quarterly, yearly — you choose.
- 🔍 Ready for Survival Analysis: Your first step to reliability modeling.
- 🛠️ Future Enhancements: Plot Nevada charts, fit survival curves, reliability forecasts.
- 🧹 Clean API: Simple, minimalistic, and powerful.

---

## 📥 Installation

```bash
pip install nevora

⚡ Quickstart

import pandas as pd
from nevora import NevadaConverter

# Load your sales/returns data
df = pd.read_csv('sales_returns.csv')

# Convert to Nevada format
converter = NevadaConverter(time_bucket='M')  # 'M' = monthly bucketing
nevada_matrix = converter.to_nevada_format(df)

# View the output
print(nevada_matrix)

🎯 Tip: You can use any custom time bucket like 'Q' (quarterly), 'Y' (yearly) based on your needs.


📈 Upcoming Roadmap

  • 🎨 Plot beautiful Nevada charts automatically
  • ⏱️ Fit Kaplan-Meier and Weibull survival models
  • 🔮 Forecast warranty returns and reliability metrics
  • 🌎 Export datasets for external tools like ReliaSoft and Minitab

💬 Why Nevora?

Warranty analysis and reliability modeling start with good data.
Nevora automates the most painful and error-prone step, letting you focus on insights — not spreadsheet wrangling.


🤝 Contributing

Pull requests, feature suggestions, and issue reports are warmly welcome.
Let's make warranty and reliability analysis effortless for everyone.


📄 License

MIT License

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