You will get Data Analysis / Machine Learning
Project details
Most people can run a model. Few can turn spectroscopic data into a result you can actually defend — that's the difference I bring.
I'm a food scientist (PhD) specializing in chemometrics and machine learning, with 15+ peer-reviewed publications in food authentication. I work daily with FTIR, UV-Vis, fluorescence and NMR data, handling the full pipeline from raw spectra to a validated, decision-ready model and a clear report.
What sets this apart:
• Real domain expertise — I understand spectroscopy and analytical data, not just generic ML
• Validation done right — cross-validation and honest metrics, so your result holds up to scrutiny
• Both worlds — classical chemometrics (PLS-DA/OPLS-DA) and modern ML (XGBoost, LightGBM), whichever fits
• Clear reporting — figures, interpretation, and on request manuscript-ready methods or documented code
Whether you need to detect adulteration, verify authenticity, classify origin, or predict a property, send me your dataset and goal and I'll tell you exactly what's achievable before you order.
I'm a food scientist (PhD) specializing in chemometrics and machine learning, with 15+ peer-reviewed publications in food authentication. I work daily with FTIR, UV-Vis, fluorescence and NMR data, handling the full pipeline from raw spectra to a validated, decision-ready model and a clear report.
What sets this apart:
• Real domain expertise — I understand spectroscopy and analytical data, not just generic ML
• Validation done right — cross-validation and honest metrics, so your result holds up to scrutiny
• Both worlds — classical chemometrics (PLS-DA/OPLS-DA) and modern ML (XGBoost, LightGBM), whichever fits
• Clear reporting — figures, interpretation, and on request manuscript-ready methods or documented code
Whether you need to detect adulteration, verify authenticity, classify origin, or predict a property, send me your dataset and goal and I'll tell you exactly what's achievable before you order.
Machine Learning Tools
H2O, Keras, MATLAB, Microsoft Excel, NumPy, pandas, Python, R, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$150
|
Standard
$400
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 12 days |
Number of Revisions | 1 | 2 | 2 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | - |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Manuscript-ready methods & results section
(+ 2 Days)
+$150
Commented R/Python source code
(+ 2 Days)
+$100
Extra model / method comparison
(+ 3 Days)
+$100Frequently asked questions
About Cagri
Data Scientist | Chemometrics, Spectroscopy & Machine Learning
Izmir, Turkey - 2:11 am local time
I'm a food scientist (PhD) with 15+ peer-reviewed publications in food authentication and chemometrics. For years I've built the full pipeline — from raw spectra to decision-ready models — and I bring that same rigor to client work.
What I do:
Spectroscopy: FTIR/ATR-FTIR, UV-Vis, fluorescence, NMR & TD-NMR
Chemometrics: PCA, PLS-DA, OPLS-DA, PLS regression, spectral preprocessing & variable selection
Machine learning: XGBoost, LightGBM, SVM, neural networks — for classification and quantification
Tools: R and Python, with thorough validation and clear reporting
Typical projects:
Detecting adulteration in juice, oil, vinegar, honey and dairy
Authenticity and geographic-origin classification
Quality-control prediction models from spectral data
Analyzing spectral datasets and preparing manuscript-ready statistics for researchers
You won't just get a model — you'll get a result you can stand behind, with the validation and documentation to support it.
If you have spectral or analytical data that needs turning into something reliable, message me and tell me about your problem. I'm glad to talk it through before you commit.
Steps for completing your project
After purchasing the project, send requirements so Cagri can start the project.
Delivery time starts when Cagri receives requirements from you.
Cagri works on your project following the steps below.
Revisions may occur after the delivery date.
Review your data and confirm the plan
I check your dataset and goal, confirm the approach and what's realistically achievable, and flag anything missing before starting.
Preprocess and explore the spectra
Cleaning, baseline correction, normalization, and exploratory analysis (PCA) to understand the structure and quality of your data.