Comparison of Classical and Quantum Machine Learning Models for diabetes

I'm looking for a freelancer skilled in both classical and quantum machine learning to analyze and compare their performances.

Key Responsibilities:
- Train Classical Models: Implement and evaluate the accuracy of models such as K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, Gaussian Naïve Bayes, and Logistic Regression.
- Train Quantum Models: Use Qiskit to implement and evaluate quantum machine learning models such as Variational Quantum Classifier (VQC) and Quantum Neural Networks (QNN).
- Performance Comparison: Create two tables; one for classical models with their accuracy scores and another for quantum models.

Deliverables:
- A Jupyter Notebook containing the implemented models and evaluations.
- A report summarizing the results and insights from the comparison.

Dataset: You will be provided with a structured dataset for model training.

Analysis Focus: The most important features for this project are accuracy and model interpretability.

Ideal Skills and Experience:
- Proficient in classical machine learning methods.
- Experienced with quantum machine learning, particularly with Qiskit.
- Familiar with model interpretability techniques.
- Capable of creating comprehensive reports and Jupyter Notebooks.

Please note, the primary focus of this project is on comparing the accuracy and interpretability of classical versus quantum models.