I am putting together my 7th-semester minor project and need a complete, ready-to-submit package that demonstrates a practical machine-learning application in electrical engineering. The exact phenomenon you forecast is flexible—what matters is that the problem is relevant to the electrical domain and that the workflow is end-to-end and clearly explained. Here is what I expect you to hand over: • Google Colab notebook – You will locate an open, reputable electrical-engineering dataset, load it in Colab, clean it, build and train an ML model, then evaluate it. Feel free to use pandas, scikit-learn, TensorFlow or PyTorch—whichever lets you reach a solid, reproducible result quickly. – The notebook must run start-to-finish without manual intervention and include inline comments that walk through the logic. • 20- to 25-slide PowerPoint presentation – Summarise the problem statement, dataset description, algorithm choice, preprocessing steps, model architecture, results, and conclusions. – Re-use plots, confusion matrices, error curves, or any other visuals generated in Colab, and add at least one explanatory flowchart of your pipeline. I have no dataset on hand, so sourcing an appropriate public dataset is part of the job. Once you identify it, cite the source in both the notebook and the slides. Turnaround is urgent: the sooner you can deliver a first draft, the better. If you have questions about scope or need to confirm the dataset before diving in, let me know right away so we stay on schedule.