Alejandro Belda Fernández — Portfolio
Computer Science graduate from the University of Murcia with a Master's degree in Artificial Intelligence. I spent one year working as a researcher (PDI) handling complex biological datasets, managing Linux server infrastructure, and collaborating in academic research environments. This gave me a strong foundation in data quality, methodological rigour, and working with noisy real-world data. I am now transitioning into industry roles in data science and data analysis.
I enjoy the full pipeline: understanding the business problem, cleaning and transforming messy data, exploring it with the right questions, building models that actually work in context, and communicating results clearly. I care about honest evaluation, documented decisions, and conclusions that non-technical stakeholders can act on.
Languages: Spanish (native) · English (B2)
Contact: alejandrobeldafernandez@gmail.com
Python library for synthetic data generation, developed during my time as a researcher. Published on PyPI and maintained independently. Designed to generate realistic tabular datasets for development, testing, and prototyping. PyPI · GitHub
Type: Classification
Stack: Python · pandas · scikit-learn · CatBoost · Optuna · SHAP
Dataset: Open data from the Madrid City Council (2019–2023)
Predicts whether a traffic accident in Madrid will result in at least one injured person, using information available at the time the accident is reported. The project covers person-to-accident aggregation, feature engineering, three models with Optuna tuning, SHAP explainability, and actionable recommendations for emergency resource allocation.
Best model (CatBoost): ROC AUC 0.873 · Balanced Accuracy 0.801 · Macro F1 0.73
What does this mean in practice? Out of every 10 accidents the model predicts as involving injuries, roughly 8 to 9 are correct. It also correctly identifies most accidents where no one is hurt, which avoids unnecessary resource dispatch. The remaining errors are mostly false alarms rather than missed injuries, which in an emergency context is the safer type of mistake.
Type: Regression
Stack: Python · pandas · scikit-learn · XGBoost · Optuna · SHAP
Dataset: Kaggle — Madrid residential property listings
Predicts the sale price of residential properties in Madrid using physical characteristics and location. The project covers data cleaning, feature engineering, TargetEncoder for district encoding, three models with Optuna tuning, confidence intervals via bootstrap and cross-validation, and SHAP explainability.
Best model (XGBoost): RMSE 69.568 € · R² 0.897 · MAPE 15.27%
What does this mean in practice? On average, the model's predicted price is within 15% of the actual sale price. For a property worth 200.000 €, the expected error is roughly 30.000 €. Built area and district are the dominant drivers — and they amplify each other: a large flat in Chamberí is worth disproportionately more than an equally large flat in Vallecas. The model is most reliable for standard properties in established districts, and less reliable for atypical or luxury properties at the extremes of the distribution.
Type: Unsupervised Clustering
Stack: Python · pandas · scikit-learn · umap-learn
Dataset: Kaggle — Customer Personality Analysis
Segments customers into 3 distinct profiles based on demographic, spending, and campaign response data. The project covers outlier detection with Isolation Forest, feature engineering, One-Hot Encoding, RobustScaler, PCA dimensionality reduction, K-Means with Elbow and Silhouette selection, UMAP visualisation, and actionable business recommendations per segment.
Final model (K-Means K=3 + PCA): Silhouette Score 0.24 · 3 segments identified
What does this mean in practice? Premium customers (high income, no children) spend €1,274 on average and respond to campaigns without needing discounts. Deal Seekers (medium income, ~1 child) buy 3.4x more when promotions are available — that is where discount budgets have the highest ROI. Window Shoppers (lowest income, most children) visit the web 6.4 times per month but spend only €80 — high intent, budget barrier. Applying the same campaign to all three simultaneously is the worst possible allocation of marketing spend.
Type: MLOps Pipeline · Binary Classification
Stack: Python · scikit-learn · Optuna · MLflow · Prefect · FastAPI · Evidently AI · PostgreSQL · Grafana · Docker · Terraform · Streamlit
Dataset: Kaggle — IBM Telco Customer Churn
End-to-end MLOps pipeline covering the full production lifecycle: data validation, feature engineering, hyperparameter optimisation with Optuna (150 trials, 5-fold CV), experiment tracking with MLflow, orchestration with Prefect, drift monitoring with Evidently AI, automated retraining, model versioning with @champion promotion, REST API with FastAPI, and an interactive demo deployed on HuggingFace Spaces.
Final model (Random Forest + Optuna): ROC-AUC 0.84 · Balanced Accuracy 0.76 · Recall (churn) 0.76
What does this mean in practice? Acquiring a new telecom customer costs 5–7× more than retaining an existing one. The model correctly identifies 76% of customers who will cancel — before they do — giving the retention team enough time to act. But the MLOps infrastructure is the actual value here: the pipeline monitors incoming data for distribution shift and retrains automatically when more than 40% of features drift. A new model only replaces the current one if it outperforms it on ROC-AUC. The result is a self-maintaining system that stays aligned with current customer behaviour without any manual intervention.
Type: Cloud Deployment · Serverless Architecture
Stack: AWS S3 · AWS Lambda · API Gateway · IAM · Python · scikit-learn
Dataset: Iris (used as a minimal case study — the focus of this project is the cloud architecture, not the model)
A small, self-contained project built to demonstrate practical AWS skills: a classification model served through a public HTTP endpoint, with no server to manage. The trained pipeline (StandardScaler + DecisionTreeClassifier) is stored in S3, loaded on demand by a Lambda function, and exposed via API Gateway. More projects coming soon.
What does this mean in practice? This project is not about the model — Iris is deliberately simple so the architecture stays the focus. It demonstrates the full serverless deployment loop end-to-end: packaging dependencies for Lambda, configuring IAM permissions, handling cold starts, and exposing a model as a public API without provisioning any server. The README documents the real issues encountered and resolved along the way (missing dependencies, Lambda package size limits, read-only filesystem errors, cold-start timeouts), since debugging a deployment is as representative of the skill as the deployment itself.
Type: Cloud Deployment · Serverless Architecture
Stack: Azure Blob Storage · Azure Functions · Python · scikit-learn
Dataset: Diabetes (scikit-learn, used as a minimal case study — the focus of this project is the cloud architecture, not the model)
A small, self-contained project built to demonstrate practical Azure skills: a regression model served through a public HTTP endpoint, with no server to manage. The trained pipeline (StandardScaler + RandomForestRegressor) is stored in Blob Storage, loaded on demand by an Azure Function, and exposed via its native HTTP trigger — no separate API Gateway needed.
What does this mean in practice? This project is not about the model — the diabetes dataset is deliberately simple so the architecture stays the focus. It demonstrates the full serverless deployment loop end-to-end: creating resources via Azure CLI (the portal was insufficient due to plan restrictions with the free subscription), registering resource providers, handling region restrictions, managing npm permissions for the Core Tools, and deploying a model as a public API without provisioning any server. The README documents the real issues encountered and resolved along the way, since debugging a deployment is as representative of the skill as the deployment itself.
Repositories in progress — publishing progressively throughout 2026.