SuperDataScience-Community-Projects/SDS-CP036-powercast

PowerCast is a time-series regression project aimed at predicting the power consumption in three urban zones of Tetouan City, Morocco, using environmental conditions such as temperature, humidity, wind speed, and solar radiation metrics. # Welcome to the SuperDataScience Community Project! Welcome to the **PowerCast: Forecasting Electricity Demand from Weather Conditions** repository! ⚡️ This project is a collaborative initiative brought to you by SuperDataScience, a global learning community focused on data science, machine learning, and AI. Whether you're just getting started or diving deeper into sequence modeling, we're excited to have you here! To contribute to this project, please follow the steps outlined in our CONTRIBUTING.md file. --- ## 📂 Repository Structure This project supports two tracks based on experience level: ``` SDS-CP036-powercast/ ├── beginner/ ← Beginner track files │ ├── README.md ← Scope of Works for Beginner Track │ ├── REPORT.md ← Markdown template for beginner submissions │ └── submissions/ │ ├── team-members/ │ └── community-contributions/ │ ├── advanced/ ← Advanced track files │ ├── README.md ← Scope of Works for Advanced Track │ ├── REPORT.md ← Markdown template for advanced submissions │ └── submissions/ │ ├── team-members/ │ └── community-contributions/ │ ├── CONTRIBUTING.md ├── requirements.txt └── README.md ← You are here! ``` --- ## 🟢 Beginner Track The **Beginner Track** is great for learners looking to build regression models from tabular environmental time-series data. You’ll analyze how weather features like temperature, humidity, and solar radiation impact power consumption in Tetouan City. You'll use models like Linear Regression, Random Forest, and XGBoost, and deploy your final model with Streamlit. 📌 Get started: ➡️ Beginner Track Scope of Works ➡️ Beginner Report Template ➡️ Submit your work --- ## 🔴 Advanced Track The **Advanced Track** is for those ready to explore sequence modeling using LSTM, GRU, or Temporal Convolutional Networks. You’ll design time-aware neural networks, experiment with lag features and …