You will get a machine learning model audit with leakage and evaluation fixes
Project details
I will audit your machine learning project and help you find technical issues that may make your results unreliable.
This service is useful if you already have a Python notebook, script, dataset, or trained model, but you are not sure whether the validation, preprocessing, metrics, or model comparison are correct.
I will check for data leakage, target leakage, wrong train/test split, weak validation strategy, preprocessing mistakes, overfitting, unsuitable metrics, class imbalance issues, and unclear model results.
Depending on the package, I can also fix the pipeline, improve the evaluation, add proper metrics, clean the source code, create useful charts, and provide a short technical report.
My focus is practical and reliable machine learning, not only high accuracy. I work with Python, pandas, scikit-learn, XGBoost, LightGBM, CatBoost, Streamlit, and FastAPI for tabular business ML projects such as fraud detection, churn prediction, forecasting, and risk prediction.
This service is useful if you already have a Python notebook, script, dataset, or trained model, but you are not sure whether the validation, preprocessing, metrics, or model comparison are correct.
I will check for data leakage, target leakage, wrong train/test split, weak validation strategy, preprocessing mistakes, overfitting, unsuitable metrics, class imbalance issues, and unclear model results.
Depending on the package, I can also fix the pipeline, improve the evaluation, add proper metrics, clean the source code, create useful charts, and provide a short technical report.
My focus is practical and reliable machine learning, not only high accuracy. I work with Python, pandas, scikit-learn, XGBoost, LightGBM, CatBoost, Streamlit, and FastAPI for tabular business ML projects such as fraud detection, churn prediction, forecasting, and risk prediction.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$50
|
Standard
$120
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 2 | 4 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code | - |
Frequently asked questions
About Peyami
Machine Learning Engineer | Python, Streamlit, FastAPI
Istanbul, Turkey - 6:30 pm local time
I help clients turn raw datasets into usable ML workflows: data cleaning, feature engineering, model training, model evaluation, dashboards, and deployment-ready prediction tools.
What I can help with:
• End-to-end ML pipelines for tabular data
• Regression and classification models using scikit-learn, XGBoost, LightGBM, and CatBoost
• Forecasting, churn prediction, fraud detection, revenue prediction, and recommendation workflows
• Streamlit dashboards for business users
• FastAPI prediction APIs for trained ML models
• Code review, debugging, cross-validation, error analysis, and leakage-safe evaluation
• Clear reports, plots, and explanations for non-technical stakeholders
Relevant project experience:
• Fraud detection ML system with XGBoost, LightGBM, CatBoost, FastAPI, and Streamlit
• Churn prediction project using leakage-safe, time-aware validation
• Movie revenue prediction and hybrid recommender using LightGBM, stacking, and metadata-based recommendation
I am also a PhD candidate in Project and Construction Management, with strong experience in research-grade modeling, structured analysis, and rigorous evaluation.
If you have a dataset, a model, or a business question, I can help turn it into a clean, reproducible, and useful ML solution.
Steps for completing your project
After purchasing the project, send requirements so Peyami can start the project.
Delivery time starts when Peyami receives requirements from you.
Peyami works on your project following the steps below.
Revisions may occur after the delivery date.
Review dataset and project goal
I will inspect the dataset, target variable, current code, model objective, and the main issue you want to solve.
Check leakage and validation
I will check for data leakage, target leakage, wrong train/test split, weak validation, and preprocessing mistakes.