You will get Machine Learning Based Solution based on your data
Project details
This project stands out by delivering clear, structured machine learning results — not just a model, but a complete proof of concept. I combine dataset analysis, filtering, model training, and testing into a focused workflow that turns raw data into actionable insights. You also receive a concise 1–2 page PDF report that explains what was built, how it performs, and what the next steps should be — making this ideal for teams that want real value, not just code.
Machine Learning Tools
Google Sheets, Keras, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, TensorFlowWhat's included $450
These options are included with the project scope.
$450
- Delivery Time 5 days
- Number of Revisions 1
- Number of Model Variations 1
- Number of Scenarios 1
- Model Validation/Testing
- Model Documentation
- Source Code
Optional add-ons
You can add these on the next page.
Additional Revision
+$99
Additional Model Variation
(+ 3 Days)
+$49
Additional Scenario
(+ 3 Days)
+$99About Viktor
PhD | Data Scientist/Analyst | AI/ ML Engineer | CV Engineer
Lviv, Ukraine - 2:23 am local time
I help companies turn complex data into reliable, scalable AI products - from early-stage concepts to production-ready systems that deliver measurable business value.
What I do best
🚀 Build custom machine learning and computer vision solutions tailored to real business needs
🧩 Design end-to-end ML pipelines: data analysis → feature engineering → modeling → validation
📊 Work with complex real-world data - images, video, 3D point clouds, IMU sensor streams, satellite imagery, numeric data, mixed data, as well as structured and large-scale textual data
🎯 Deliver clear, decision-ready results, not just code
Technical stack
Python, Machine Learning, Deep Learning, Computer Vision, 3D Data Processing, Point Clouds,
Time-Series & Signal Processing, Remote Sensing, Recommendation Systems,
LLM-based systems (RAG pipelines, AI chatbots), Data Analytics, Model Evaluation, and Production-oriented ML Pipelines.
Background
🎓 My background combines applied engineering and academic research. Alongside commercial projects in industrial inspection, smart agriculture, motion recognition, satellite-based monitoring, and predictive modeling, I also build LLM-powered systems - including RAG pipelines, AI chatbots, and intelligent assistants for real-world business use cases.
In parallel, I conduct PhD-level research in Artificial Intelligence, specializing in neural networks for unstructured and noisy data, which allows me to bridge advanced research with production-ready engineering.
📫 Let’s connect!
I’m always open to collaboration, interesting projects, or a good discussion about emerging technologies in AI and data science.
Steps for completing your project
After purchasing the project, send requirements so Viktor can start the project.
Delivery time starts when Viktor receives requirements from you.
Viktor works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & goal alignment
Review your dataset and confirm the objective + success metric
Data audit & quality checks
Inspect missing values, outliers, leakage risks, class imbalance, and target definition