Master 1 Business Intelligence student · Data Analytics, Python & Financial Data Yaoundé, Cameroon · LinkedIn · tchomtchidaniel@gmail.com
I build Python tools that turn raw financial and operational data into something people can use: dashboards, controls, indicators and ready-to-share reports. I am especially interested in payment systems, banking data and decision support.
En bref Étudiant en Master 1 Business Intelligence à l'IUSJ, titulaire d'une Licence en Management et Techniques Quantitatives (mémoire soutenu avec la mention « Excellent », septembre 2026). Je développe des applications Python orientées analyse de données, automatisation et reporting, en particulier pour la finance et les systèmes de paiement.
- Data / Payment Systems intern — BEAC, Payment Systems and Instruments Department (Jun–Aug 2026). Designed DAN, an application that structures and automates part of the off-site (document-based) supervision of payment systems. The public repository below is an independent demo on synthetic data.
- Intern — Africa Finance (Jul–Aug 2025) and Afriland First Bank (Jul–Aug 2024): customer relations, account opening and management.
- Startup Weekend IUSJ 2026 — First Prize, technical lead of the Health Care project.
- Education — Master 1 Business Intelligence (in progress) and Licence in Management & Quantitative Techniques (2023–2026), Institut Universitaire Saint Jean du Cameroun (IUSJ).
| Project | What it does | Stack |
|---|---|---|
| dan-payment-risk-analysis | Turns monthly payment-system reporting forms into risk scores, incident analysis and Word/PDF reports. Synthetic demo data, 200+ tests and CI. | Python, Streamlit, pandas, python-docx, pytest |
| gestion-salaires | Teacher payroll management: pay-period cycle, payroll controls, Word payslips, Excel accounting exports, roles and audit trail. 1,300+ tests and CI. | Python, Streamlit, SQLite, openpyxl, python-docx, pytest |
| consolidateur-liasses-paiements | Local app that consolidates multi-sheet Excel payment-statistics returns, computes indicators, draws charts and exports the results. Optional local LLM (Ollama) only labels template rows, never touches amounts. | Python, Streamlit, pandas, Plotly, openpyxl |
| Project | What it does | Stack |
|---|---|---|
| prediction_fraude | Credit-card fraud detection: class-imbalance handling (SMOTE & co.), model comparison, evaluation metrics. | scikit-learn, imbalanced-learn, Streamlit |
| projet_census1 | Predicts whether income exceeds 50K USD from Census data, comparing several classifiers. | scikit-learn, Streamlit |
| depression-prediction-app | Student depression risk score from lifestyle and academic factors (educational project). | scikit-learn, Streamlit |
| iris-dashboard-ia9 | Interactive exploration and species prediction on the Iris dataset. | Streamlit, seaborn |
Daily hours and work sessions: work log (updated every night from WakaTime).
Used in my projects: Python · pandas · NumPy · Streamlit · SQLite · scikit-learn · Plotly · matplotlib · seaborn · openpyxl · python-docx · pytest · Git & GitHub Actions
Currently strengthening: SQL · statistics · Power BI · time series
Languages: French (native) · English (B1) · German (beginner)