You will get a machine learning model to predict outcomes from your data
Project details
I build reliable machine learning models to predict outcomes from your data, with a clear and structured workflow focused on correctness, transparency, and reproducibility.
The project covers preprocessing, model training, and evaluation, with model selection performed across multiple candidates before choosing and assessing one final model. The scope is clearly defined, and optional add-ons are available for data preparation, advanced evaluation, documentation, or non-technical summaries.
You receive clean, reproducible source code in a Jupyter notebook, along with optional exports and the trained model upon request. The goal is to deliver a well-tested prediction model that fits your data and objectives.
The project covers preprocessing, model training, and evaluation, with model selection performed across multiple candidates before choosing and assessing one final model. The scope is clearly defined, and optional add-ons are available for data preparation, advanced evaluation, documentation, or non-technical summaries.
You receive clean, reproducible source code in a Jupyter notebook, along with optional exports and the trained model upon request. The goal is to deliver a well-tested prediction model that fits your data and objectives.
Machine Learning Tools
NumPy, Python, Python Scikit-Learn, scikit-learn, SciPy, XGBoostWhat's included
| Service Tiers |
Starter
$149
|
Standard
$299
|
Advanced
$549
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 2 | 2 | 2 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 0 | 0 | 3 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$49 - $119
Additional Revision
+$39
Additional Model Variation
(+ 1 Day)
+$75
Additional Scenario
(+ 2 Days)
+$99
Additional Graph/Chart
+$15
Model Documentation
(+ 1 Day)
+$79
Executive Summary & Model Explanation
(+ 1 Day)
+$79
Data Preparation & Exploratory Analysis
(+ 2 Days)
+$170Frequently asked questions
About Sulaiman
Data Scientist | Applied AI & End-to-End ML Solutions
Fresnes, France - 2:09 pm local time
I work across the full data science lifecycle, including data validation and cleaning, exploratory data analysis, hypothesis testing when relevant, preprocessing, feature engineering, model development, and deployment when required. I hold a Master of Engineering (MEng) in Computer Science, specialized in Artificial Intelligence.
I have experience delivering data analysis and predictive modeling projects using structured, Python-based workflows. I place strong emphasis on proper problem formulation, metric selection, and transparent evaluation so that results are technically sound and aligned with business objectives. Where appropriate, I support the transition from experimentation to reliable use by structuring reproducible pipelines and workflows.
Clear communication and well-defined scope are important from the start of a project. Deliverables are designed to be reproducible and understandable for both technical and non-technical audiences. I can also provide supporting documentation or summary reports to facilitate stakeholder alignment.
If you are looking for a data scientist who values rigor, clarity, and practical outcomes, I would be glad to discuss your project and requirements.
Public portfolio examples are selected to illustrate my approach and methodology. Some professional work cannot be shared due to confidentiality requirements.
Steps for completing your project
After purchasing the project, send requirements so Sulaiman can start the project.
Delivery time starts when Sulaiman receives requirements from you.
Sulaiman works on your project following the steps below.
Revisions may occur after the delivery date.
Scope confirmation & setup
Confirm the prediction objective, target definition, evaluation metric, and agreed scope (including any selected add-ons) before starting development.
Model development
Develop the preprocessing and training pipeline, tune and compare multiple model configurations, and select the best-performing model according to the agreed metric.