You will get a Python machine learning pipeline for clinical or biomedical data analysis


Project details
You will get a clean, documented Python machine learning pipeline built specifically for your clinical or biomedical dataset. With a background in deep learning, graph-based models, and real-world EHR data, I build pipelines that are reproducible, well-structured, and ready for further development or deployment.
Deliverables include preprocessing code, model training and evaluation scripts, performance metrics, and a short technical summary of findings.
This service covers supervised ML development on the provided datasets. I do not provide data collection, hospital system integration, regulatory compliance advice, or FDA/TGA approval guidance.
Deliverables include preprocessing code, model training and evaluation scripts, performance metrics, and a short technical summary of findings.
This service covers supervised ML development on the provided datasets. I do not provide data collection, hospital system integration, regulatory compliance advice, or FDA/TGA approval guidance.
Machine Learning Tools
NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$400
|
Standard
$900
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 10 days | 20 days | 30 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 5 |
Number of Scenarios | 1 | 2 | 4 |
Number of Graphs/Charts | 3 | 6 | 10 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Graph/Chart
(+ 2 Days)
+$50About Aashish
Medical AI Consultant | ML for Healthcare | PhD Researcher
Preston, Australia - 1:27 am local time
Steps for completing your project
After purchasing the project, send requirements so Aashish can start the project.
Delivery time starts when Aashish receives requirements from you.
Aashish works on your project following the steps below.
Revisions may occur after the delivery date.
Data audit
Review dataset structure, check for missing values and quality issues
Preprocessing
Clean, encode and engineer features