Dedicated Analysis Service

Machine Learning, Deep Learning, Data Mining & Explainable AI (XAI)

Predictive modeling, classification, clustering, NLP sentiment/topic modeling, and SHAP feature attribution in Python, R, and MATLAB.

In modern academic research, black-box accuracy is not enough; reviewers demand nested cross-validation, hyperparameter tuning, and Explainable AI (SHAP/LIME) alongside reproducible Python/R scripts.

Python Scikit / XGBoost

Supervised Learning (XGBoost, LightGBM, Random Forest, SVM, MLP)

Feature engineering, SMOTE balancing, Bayesian hyperparameter optimization, K-fold stratified cross-validation, and benchmarking ensemble learners against classical baselines.

Required Data & Inputs

Dataset (CSV/Excel) with a defined target variable (continuous or categorical).

Dedicated Software
Python (Scikit-Learn / XGBoost)R (caret / tidymodels)MATLAB
What You Receive (Deliverables)

Comparative metrics table (Accuracy, F1, ROC-AUC or RMSE, MAE, R²), confusion matrices, ROC/PR curves, and clean Python/R notebooks.

Explainable AI (XAI)

Explainable AI (SHAP Beeswarm, Waterfall, LIME & PDP)

Opening black-box ML models using game-theoretic Shapley values (SHAP): global feature importance, beeswarm summary plots, dependence interaction plots, and local waterfall explanations.

Required Data & Inputs

Training and test datasets used in the predictive model.

Dedicated Software
Python (shap / lime)R
What You Receive (Deliverables)

High-resolution SHAP Beeswarm, Bar, Waterfall, and PDP figures with academic interpretation.

PyTorch / TensorFlow

Deep Learning Time Series (LSTM, CNN-BiLSTM) & Clustering (K-Means, DBSCAN)

Recurrent and Attention-based neural architectures (LSTM/GRU) for complex time-series forecasting, plus unsupervised clustering validated via Silhouette, Davies-Bouldin, and Elbow indices.

Required Data & Inputs

Sequential time-series data or unlabeled feature matrix for clustering.

Dedicated Software
Python (PyTorch / TensorFlow)MATLAB
What You Receive (Deliverables)

Train/validation loss curves, actual-vs-predicted plots, Silhouette diagrams, cluster profile tables, and source code.

⬡ The Metoda 4-Part Delivery Standard
01Raw file & executable script

Project file, Do-file / R & Python scripts — 100% reproducible results

02APA 7th standard tables

Editable Word file, ready for chapter 4 or an ISI/Scopus paper

03Print-quality vector figures

Path models, causal plots, volatility & Pareto — 300 DPI / SVG

04Step-by-step narrative & support

Full statistical interpretation + support until defense or acceptance

Your method isn’t in this list? — we execute any method on any software.

Other Analysis Service Categories

Quantitative Statistical Analysis & Hypothesis TestingStructural Equation Modeling (PLS-SEM & CB-SEM)Qualitative Data Analysis & Mixed-Methods ResearchMulti-Criteria Decision Making (MCDM), Fuzzy Logic & ISM/TISMEconometrics, Time Series, Volatility Modeling & Panel DataComputer Architecture Sim, System Simulation, DEA & Mathematical OptimizationScientometrics, PRISMA Systematic Reviews & Quantitative Meta-Analysis

Ready to start your research data analysis?

Send your project details in Telegram; data review, timeline, and pricing happen in the same conversation.

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