You will get Build a Production ML Benchmark - Data to Deployment (Like Readmit-Bench)
Project details
You send me a CSV I send you back a production-ready ML service. Any domain, any tabular problem.
Whether you're predicting churn, fraud, sales, defaults, patient outcomes, lead conversion, or anything else with a target column the workflow is the same. I test every algorithm that makes sense for your data: linear baselines (Logistic/Linear Regression, Ridge), tree ensembles (Random Forest, Extra Trees), gradient boosters (XGBoost, LightGBM, CatBoost), and neural networks when the dataset is large enough. Identical train/test splits, identical metrics, honest comparison.
The winner is picked by the numbers ROC-AUC, F1, RMSE, MAE, calibration whatever fits your problem. You get the full leaderboard, every evaluation chart, the trained model, and clean Python code. Yours forever, no licensing, no black box.
Previously shipped end-to-end for hospital readmission prediction (live demo available on request) same pipeline, proven in production.
Whether you're predicting churn, fraud, sales, defaults, patient outcomes, lead conversion, or anything else with a target column the workflow is the same. I test every algorithm that makes sense for your data: linear baselines (Logistic/Linear Regression, Ridge), tree ensembles (Random Forest, Extra Trees), gradient boosters (XGBoost, LightGBM, CatBoost), and neural networks when the dataset is large enough. Identical train/test splits, identical metrics, honest comparison.
The winner is picked by the numbers ROC-AUC, F1, RMSE, MAE, calibration whatever fits your problem. You get the full leaderboard, every evaluation chart, the trained model, and clean Python code. Yours forever, no licensing, no black box.
Previously shipped end-to-end for hospital readmission prediction (live demo available on request) same pipeline, proven in production.
Machine Learning Tools
pandas, Python Scikit-Learn, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 2 | 4 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Frequently asked questions
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JC
John C.
Oct 15, 2024
Image Curation & Data Entry
SS
Sait S.
Jun 10, 2024
Data Entry Needed for Restaurant Menu
I highly recommend this freelancer! The work was completed quickly and efficiently, with exceptional attention to detail. It was a pleasure working with such a dedicated professional
GO
Gegenration O.
Oct 29, 2023
Data Collection & Entry
Ibrahim has helped me with various tasks. Even the tasks he never done before, he was able to learn it fast and deliver high quality.
About Ibrahim
Bioinformatician | Python & R | RNA-Seq | Drug Discovery
Kafr az Zayyat, Egypt - 10:00 am local time
Over the past few years, I’ve completed academic and personal projects in Alzheimer’s and Parkinson’s disease research, cancer genomics, and multi-omics integration. I also built QSARify.com — a web-based platform for QSAR modeling and drug repurposing.
Core Expertise
- Single-Cell & Bulk RNA-Seq (Seurat, DESeq2, Harmony, MiloR, CellChat)
- Genomics & Transcriptomics Data Analysis
- Structure-Based Drug Discovery (AutoDock Vina, AlphaFold, Swiss-Model)
- Machine Learning for Biology (scikit-learn, pandas, R)
- Bioinformatics Pipelines on Linux / HPC / Docker
- Data Visualization (ggplot2, matplotlib, seaborn)
- Web App Deployment (Flask, Shiny)
What You Can Expect
- Clean, reproducible analysis workflows
- Interactive visual reports and documentation
- Transparent communication and timely delivery
I’m currently pursuing a B.Sc. in Biotechnology (Bioinformatics Major) at Nile University (expected 2025) and have participated in multidisciplinary research projects linking computational biology, AI, and molecular medicine.
Steps for completing your project
After purchasing the project, send requirements so Ibrahim can start the project.
Delivery time starts when Ibrahim receives requirements from you.
Ibrahim works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & data review
I review your dataset, confirm the prediction target and problem type (classification, regression, ranking), and flag any data quality issues. You get a short readiness report within 24 hours.
Model benchmark
Every relevant algorithm trained on identical splits linear models, tree ensembles, gradient boosters (XGBoost/LightGBM/CatBoost), and neural nets when warranted. You receive the full leaderboard with all key metrics.