Metadata-Version: 2.4
Name: enerlytics
Version: 0.1.0
Summary: Renewable energy insights dashboard — data-driven exploration of energy systems and consumption patterns.
Author-email: Huseyin Kucukogul <huseyinkucukogulcontact@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Huseyin
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        The above copyright notice and this permission notice shall be included in all
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Project-URL: Homepage, https://github.com/Kucukogul/enerlytics
Project-URL: Repository, https://github.com/Kucukogul/enerlytics
Keywords: energy,renewable energy,data science,dashboard,analytics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.5.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: plotly>=5.0.0
Requires-Dist: streamlit>=1.20.0
Dynamic: license-file

# 🌍 Renewable Energy Insights Dashboard

The **Renewable Energy Insights Dashboard** is a data analysis project focused on the **United States**, exploring how renewable energy production — including solar, wind, hydroelectric, geothermal, and biomass — has evolved from 1973 to 2024. By transforming raw U.S. Energy Information Administration (EIA) data into interactive insights, this project supports data-driven decisions for a sustainable energy future.

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## 🎯 Objectives

- Analyze U.S. renewable energy production by **year, energy source, and sector** (e.g., electric power, industrial)
- Identify long-term **growth trends** and **structural shifts** in energy mix
- Create **interactive visualizations** to reveal patterns and anomalies
- Deliver insights through a user-friendly **Streamlit dashboard**

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## 🧩 Tech Stack

| Category         | Tools / Libraries                     |
|------------------|---------------------------------------|
| Programming      | Python                                |
| Data Handling    | Pandas, NumPy                         |
| Visualization    | Matplotlib, Seaborn, Plotly           |
| Dashboard        | Streamlit                             |
| Deployment       | Streamlit Cloud / Render              |
| Version Control  | Git & GitHub                          |

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## 📊 Features

✅ Clean and aggregate monthly EIA data into **annual totals**  
✅ Visualize **historical trends (1973–2024)** by energy type  
✅ Compare contributions of **solar, wind, hydro, geothermal, biomass, and waste**  
✅ Interactive Streamlit dashboard with dynamic filtering  
✅ Modular design — ready to extend with **time-series forecasting** (e.g., Prophet, ARIMA)
