I have a clean, working preprocessing pipeline for the PIMA Indians Diabetes dataset and now want to move straight into model-building and evaluation. The core requirement is to implement a Support Vector Machine classifier in scikit-learn and compare its performance against at least two additional scikit-learn algorithms of your choice. You are free to pick whichever alternatives you believe will showcase meaningful contrasts—just keep everything within the scikit-learn ecosystem so the code integrates smoothly with my existing workflow. You will receive: • the prepared feature matrix and labels generated by my pipeline • a short spec outlining the exact train/test split already in place I am expecting: • well-structured Python scripts or notebooks that load the data, fit the SVM and the comparative models, and output accuracy, precision, recall, F1-score, ROC-AUC, and a confusion matrix • concise comments so every major step is clear • a brief summary (Markdown or PDF) interpreting the results and highlighting which model performs best and why Keep the solution reproducible—random seeds set and any hyper-parameter tuning done through GridSearchCV or equivalent. Once I can run the code end-to-end on my machine and replicate the metrics you report, the job is complete.