What’s New
-Developed a Colab Notebook where users can generate datasets, train models, and visualize results.
-Implemented a Linear Regression baseline model with ~68% accuracy (R² ≈ 0.68).
-Added visualizations comparing actual vs predicted loads to clearly show forecasting performance.
-Designed a clean flowchart to explain our pipeline: Data → Preprocessing → Model Training → Forecast.
-Created a project thumbnail & graphics to make the submission more engaging.
Features So Far
-Data preprocessing with Python, Pandas, NumPy
-Forecasting using Scikit-learn
-Visualization via Matplotlib
-Beginner-friendly deployment using Google Colab
What’s Next
-Explore advanced models like LSTM/GRU for improved accuracy.
-Deploy the forecasting tool as a Streamlit web app for interactive use.
-Add support for renewable load forecasting (solar, wind,etc).
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