Heart disease prediction
Budget: ₹12,500 – ₹37,500 INR
I have a curated dataset of patients’ genetic profiles and need a deep-learning solution that can reliably flag the presence of heart disease. Because the data are entirely genomic, the job begins with thoughtful preprocessing and feature engineering (handling high-dimensional SNPs, normalisation, train/validation split, class-imbalance techniques if required).
My single, overriding success metric is F1 Score; accuracy alone will not do, so the model must be tuned to balance precision and recall. You may choose the exact framework—PyTorch or TensorFlow/Keras are both fine—as long as the final code is clean, reproducible, and GPU-ready.
Deliverables
• End-to-end Python code or notebook that loads the raw genetic data, performs preprocessing, trains the deep neural network, and outputs predictions.
• Saved, versioned model weights plus a short README on how to retrain or fine-tune.
• Evaluation report that highlights F1 Score, precision, recall, and confusion matrix on the held-out test set.
• Brief architecture rationale so I understand why your chosen layers, regularisation, and hyperparameters suit genomic inputs.
I will provide the dataset and any relevant phenotype labels as soon as we start. My priority is a clear, well-documented workflow that I can deploy or extend later, so please keep modularity and readability in mind throughout the project.
My single, overriding success metric is F1 Score; accuracy alone will not do, so the model must be tuned to balance precision and recall. You may choose the exact framework—PyTorch or TensorFlow/Keras are both fine—as long as the final code is clean, reproducible, and GPU-ready.
Deliverables
• End-to-end Python code or notebook that loads the raw genetic data, performs preprocessing, trains the deep neural network, and outputs predictions.
• Saved, versioned model weights plus a short README on how to retrain or fine-tune.
• Evaluation report that highlights F1 Score, precision, recall, and confusion matrix on the held-out test set.
• Brief architecture rationale so I understand why your chosen layers, regularisation, and hyperparameters suit genomic inputs.
I will provide the dataset and any relevant phenotype labels as soon as we start. My priority is a clear, well-documented workflow that I can deploy or extend later, so please keep modularity and readability in mind throughout the project.