samuel@portfolio:~/projects % cat geopolitical-crisis-narrative-tracker.md
flagship project
Geopolitical Crisis Narrative Tracker
A polyglot data engineering system that ingests global news from 43 outlets in 17 languages, stores it across four database paradigms, and exposes a LangChain agent that analyses how different countries and media outlets frame the same geopolitical crisis. Usable from a user-friendly interface built using the tkinter Python library.
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Problem
The system addresses a question that matters in finance, intelligence, and journalism: how do BBC, Al Jazeera, Xinhua, Fox News, and TASS differ in covering the same conflict — and what do those patterns reveal about narrative, bias, and escalation risk?
Data sources
Six source categories feed the pipeline across 17 languages: GDELT DOC 2.0 + GKG APIs (event data and tone scores), 43 RSS outlets spanning every continent (BBC, Al Jazeera, TASS, Xinhua, Fox News, Le Monde, NDTV and others) scraped via feedparser, Wikipedia and ReliefWeb APIs for context, and a UN Security Council BeautifulSoup scraper for resolutions.
Architecture
Airflow DAGs orchestrate ingestion on 15-minute, hourly, and daily schedules. Python ETL applies spaCy NER, LibreTranslate for 17-language normalisation, and entity resolution before routing records to the appropriate storage layer. OpenLineage instruments every transformation and emits provenance events to a Marquez lineage store.
Storage
Four databases handle different access patterns: PostgreSQL 16 for structured GDELT event data and tone time-series, MongoDB 7 for raw article documents with flexible per-outlet schemas, Neo4j Community for the actor–event–source–country relationship graph (2,500+ actors and 10,000+ relationships), and ChromaDB for sentence-transformer embeddings (5,500+ chunks) supporting semantic RAG retrieval.
Agent tools
The LangChain agent exposes seven tools backed by the four databases: semantic narrative search (ChromaDB), tone timeline queries (PostgreSQL), variable-depth actor graph traversal (Neo4j), cross-outlet coverage comparison (MongoDB aggregation), live humanitarian context (ReliefWeb), Wikipedia background lookup, and GeoJSON conflict-hotspot retrieval for the Leaflet map. Supports Ollama, OpenAI, Anthropic, Gemini, and Together AI providers.
API
FastAPI exposes twelve REST endpoints plus an MCP tool-discovery interface, containerised in Docker Compose alongside the eight-service stack (Postgres, Mongo, Neo4j, Chroma, Airflow, LibreTranslate, Marquez, FastAPI). Includes a Leaflet conflict map at /map served from PostgreSQL GeoJSON. The stack is deployable to GCP Cloud Run.
Testing
216 tests covering ingestion correctness, storage schemas, every API endpoint, agent tool contracts, OpenLineage lineage event emission, and Airflow DAG integrity. Run via `docker exec geo_fastapi python -m pytest tests/ -v`.
Key takeaway
The strongest signal from this project is the architectural judgement: each database was chosen for a specific access pattern, not for coverage. The tooling follows the problem — a principle that holds up under interview scrutiny.
schema summary
PostgreSQL 16Time-series + structured analytics
Referential integrity and date-range queries for tone trajectories
GDELT events, tone scores, sources, conflicts, actors
MongoDB 7Flexible per-outlet schemas
Aggregation pipelines for cross-outlet coverage comparison
3,500+ raw articles, 44 UN documents
Neo4j Community2,500+ actors · 10k+ relationships across 3,400+ articles
Multi-hop traversals, centrality analysis, ego networks
Actors · events · sources · countries
ChromaDB5,500+ vectors with stance / region / language metadata
Semantic RAG retrieval for the LangChain agent
Article-chunk embeddings (sentence-transformers)
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snapshot
- —43 RSS outlets · 17 languages
- —PostgreSQL · MongoDB · Neo4j · ChromaDB
- —5,500+ article embeddings
- —Neo4j: 2,500+ actors · 10k+ relationships
- —LangChain agent with 7 tools
- —Airflow orchestration
- —OpenLineage / Marquez lineage
- —FastAPI + MCP (12 endpoints)
- —Docker Compose (8 services)
- —spaCy NER · sentence-transformers · LibreTranslate
- —216 tests across ingestion, storage, API, agent
pipeline_architecture.png↗ expand
pipeline_runner_gui.png↗ expand
polyglot_schema.png↗ expand
actor_cooccurrence_network.png↗ expand
tone_by_outlet.png↗ expand
regional_bias_heatmap.png↗ expand
attention_vs_reality.png↗ expand
conflict map
static_event_map.png↗ expand
samuel@portfolio:~/projects % cat indie-game-success-data-pipeline.md
project 02
Indie Game Success Data Pipeline
An end-to-end data engineering project analysing the commercial success of indie games on Steam through a reproducible multi-source pipeline.
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Problem
The project set out to analyse what commercial success looks like for indie games on Steam using a pipeline that could handle multiple external signals and keep the final dataset analytically useful.
Data sources
The pipeline combines eight sources, including Steam data, Steam Charts scraping, Google Trends, Reddit sentiment, and additional metadata sources so the final dataset reflects both platform and external demand signals.
Architecture
The system follows a practical engineering structure: collect and scrape source data, standardise and process it in Spark, persist intermediate and final data in storage layers, then expose clean analytical outputs for downstream use.
Pipeline / processing
Apache Spark handled the main processing workflow. Samuel focused on debugging and building the Spark pipeline so the data could move from raw source collection into a consistent feature-ready dataset.
Storage
PostgreSQL and MongoDB were used as storage layers, with Parquet and DuckDB supporting the analytics layer. This gave the project a clear split between operational storage and analysis-friendly formats.
Analytics outputs
The result was a master dataset with roughly 62 features and a classification approach that grouped games into three success tiers based on estimated owner counts at 20k, 200k, and 1M.
Samuel's contribution
Samuel was responsible for Google Trends data collection, Steam Charts scraping, and substantial Spark pipeline debugging and build work. The portfolio should emphasise these direct contributions rather than the work of the broader group.
Key takeaway
The strongest signal from this project is not a headline metric. It is the ability to build and stabilise a real multi-source pipeline that turns messy external data into a decision-ready analytical dataset.
schema summary
PostgreSQL~150,000 rows
Structured data, normalised dimensions, time series
11 data tables + 2 Spark output tables + 1 view
MongoDB2,432 docs / ~113,000 nested records
Nested reviews and posts with sub-documents
steam_reviews, reddit_posts
Parquet1,216 rows each
Columnar output for DuckDB queries
master_dataset, sentiment_scores, pre_launch_signals
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@@@@@@
snapshot
- —1,216 indie games
- —8 integrated data sources
- —PostgreSQL + MongoDB storage
- —Apache Spark processing
- —Parquet + DuckDB analytics
- —Success tier classification
pipeline_architecture.png↗ expand
postgres_schema.png↗ expand
mongodb_schema.png↗ expand
samuel@portfolio:~/projects % cat premier-league-predictive-analytics.md
project 03
Premier League Predictive Analytics
A predictive analytics project built on 15 seasons of Premier League data, combining match prediction, bookmaker benchmarking, and season simulation.
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Problem
The aim was to predict three-way match outcomes in the Premier League while testing the models against the strongest baseline in the space: bookmaker-implied probabilities.
Data sources
The data stack combines 15 seasons of match data with ClubElo ratings, transfer spending, expected goals data, and manager tenure information.
Architecture
The workflow runs from source ingestion and feature engineering into walk-forward training, probability calibration, ensemble blending, and season-level simulation.
Pipeline / processing
The processing stage builds rolling form, goal difference, days rest, Elo momentum, shots-on-target, and implied probability features before training Logistic Regression, LightGBM, and MLP models with temporal decay weighting.
Storage
This project is best understood as a reproducible analytics workflow rather than a storage-heavy data engineering system. The value lies in pipeline structure, feature design, and honest evaluation.
Analytics outputs
Outputs include calibration plots, feature importance, model comparison metrics, league table forecasts, and Dixon-Coles Monte Carlo simulations for full-season outcomes.
Samuel's contribution
Samuel's contribution sits across the full modelling workflow: assembling and cleaning inputs, engineering and testing features, evaluating models against bookmaker baselines, and presenting the results through reproducible figures and comparisons.
Key takeaway
The strongest signal from the project is not a claim of beating the market outright. It is the ability to build a disciplined predictive pipeline, benchmark it correctly, and identify where blending or simulation adds real analytical value.
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snapshot
- —15 seasons, 5,700 matches
- —Bet365 and Pinnacle benchmarks
- —Best single model: 0.9678 log-loss
- —Best blend beats Bet365, p=0.007
- —Dixon-Coles season simulation
- —6.7/20 exact positions
feature_pipeline.png↗ expand
metrics_comparison.png↗ expand
samuel@portfolio:~/projects % cat marketing-analytics-segmentation.md
segmentation output — 3 clusters
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■■ ■ ■■ project 04
Marketing Analytics Segmentation
A segmentation-oriented analytics project focused on turning raw customer data into clearer groupings for decision-making.
Problem
The objective was to identify useful structure in customer data so that analysis could move from broad averages to more actionable groupings.
Data sources
The project works with customer or marketing data shaped into a form suitable for segmentation analysis and downstream interpretation.
Architecture
This is best represented as a clean analytics workflow: prepare the dataset, create segment-relevant features, test clustering or grouping logic, and present interpretable outputs.
Pipeline / processing
The emphasis is on reproducible preparation and careful feature design so that segmentation results remain understandable rather than opaque.
Storage
Storage detail should stay lightweight here, since the portfolio value lies more in analytical structure than in infrastructure complexity.
Analytics outputs
Outputs focus on segment definitions, comparative patterns between groups, and the practical use of those distinctions in analysis.
Samuel's contribution
Samuel's contribution should be framed around analytics design, feature work, and interpretation rather than claims about dramatic commercial uplift.
Key takeaway
This project reinforces Samuel's ability to translate raw data into decision-ready structure, which aligns well with analytics engineering roles.
snapshot
- —Customer segmentation
- —Analytics workflow
- —Feature framing
- —Decision-ready outputs
