You will get a Python Machine Learning Classification Model
Project details
You will get:
• Trained classification model (Python, scikit-learn or TensorFlow)
• Well-commented source code for the full workflow: data cleaning, feature engineering, training, evaluation
• Accuracy report with relevant metrics (accuracy, precision, recall, confusion matrix)
• Instructions on how to run the model and get predictions on new data
• Unlimited support through Upwork messages
• Trained classification model (Python, scikit-learn or TensorFlow)
• Well-commented source code for the full workflow: data cleaning, feature engineering, training, evaluation
• Accuracy report with relevant metrics (accuracy, precision, recall, confusion matrix)
• Instructions on how to run the model and get predictions on new data
• Unlimited support through Upwork messages
Machine Learning Tools
Keras, NumPy, pandas, Python, Python Scikit-Learn, TensorFlowWhat's included
| Service Tiers |
Starter
$50
|
Standard
$120
|
Advanced
$220
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 10 days |
Number of Revisions | 2 | 3 | 5 |
Number of Model Variations | 1 | 3 | 3 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$15 - $50
API Deployment (FastAPI)
(+ 5 Days)
+$50Frequently asked questions
About Wincent
AI/ML Engineer | End-to-End Machine Learning Systems
Medan, Indonesia - 4:48 am local time
I build machine learning systems from the ground up, starting with messy, real-world data and ending with something that actually works in production. That means cleaning and preprocessing data, engineering the right features, training models in Python and TensorFlow, and tracking every experiment properly so you know exactly what worked, what didn't, and why. I don't just hand off a model file and disappear, I document the process, explain the tradeoffs, and make sure you understand what you're actually getting.
What sets me apart from most ML freelancers is my background. Before moving into AI engineering, I spent 2 years doing design work for 10+ brands. That means I can also make your results understandable and presentable to the people on your team who aren't going to read a confusion matrix for fun, whether that's a founder, a stakeholder, or an investor.
📊 What I've built:
- Achieved 88% test accuracy (89% training) predicting lung cancer risk from 5,000 records using KNN (k=20), published in JUMINTAL (DOI: 10.55123/jumintal.v4i1.5460) and integrated into the LungHealth mobile app
- Automated end-to-end retraining and deployment of a clinical Random Forest model (303 records, 14 features) via GitHub Actions, tracking 9 hyperparameter configurations in MLflow and containerizing via Docker
- Developed a 6-class image classifier on 14,000+ images using EfficientNetB0, achieving 94.96% training (93.96% test) accuracy, compiled into SavedModel, TF-Lite, and TFJS formats
Implemented ML pipelines and web applications using Python, TensorFlow, scikit-learn, MLflow, Docker, FastAPI, React, and TypeScript
⚙️ How I work:
Get into the data first. Before writing a line of model code, I figure out what's actually predictable from what you've got, and I'll tell you honestly if something isn't feasible
Build it properly. Preprocessing, feature engineering, and training, done in Python with scikit-learn or TensorFlow, no shortcuts
Track everything. Every experiment logged in MLflow, every deployment containerized in Docker, so nothing is a black box you have to trust blindly
✅ Why work with me:
I won't oversell what a model can do. If your data can't support what you're hoping for, I'll tell you early, not after you've paid for something that quietly underdelivers. I'd rather set realistic expectations upfront than have you find out the hard way.
Not sure if your data can support what you need? That's exactly the kind of question I like to dig into!
Steps for completing your project
After purchasing the project, send requirements so Wincent can start the project.
Delivery time starts when Wincent receives requirements from you.
Wincent works on your project following the steps below.
Revisions may occur after the delivery date.
Review data and confirm scope
I'll inspect your dataset, check data quality, and confirm what's realistically predictable before starting any modeling.
Clean and prepare the data
Handle missing values, outliers, and encoding, then engineer relevant features for the model.