Who am I?
Data Science & AI Engineer • ESPRIT
I build intelligent products that connect math, code, and people. I work in ML, NLP, recommendation systems, and AI-powered web apps.
AI & Data
Designing intelligent systems that learn from data and create impact.
I'm a Data Science & AI Engineer focused on turning real-world problems into deployable ML solutions. Explore my portfolio to see projects leveraging machine learning, deep learning, and MLOps.
Natej Ghodbane
Data Science & AI Engineer
4+
Projects
2+
Years
100%
Dedicated
Machine LearningDeep LearningMLOps
About me
Data-driven, user-centric, curious.
Core Skills
What I use to craft ideas.
PythonPandasScikit-learnTensorFlowNLPRAGSQLMongoDBPostgresNext.jsGitLinuxMLOpsCI/CDDockerJenkinsmlflowELk
Selected work
Projects
MLOps • AI
Churn Prediction MLOps Platform
End-to-end MLOps platform for customer churn prediction with automated training, MLflow experiment tracking, FastAPI inference, Streamlit dashboard, ELK Stack, and Dockerized CI/CD pipeline.
PythonXGBoostMLflowFastAPIStreamlitDockerJenkinsELK Stack
BI Dashboard • ML
Restaurant Rating Analysis Dashboard
Interactive Power BI dashboard analyzing restaurant satisfaction, customer profiles, cuisine preferences, pricing, parking availability, and transportation methods. The project also includes Python-based machine learning visuals to study the factors influencing overall ratings and cuisine prediction.
- Built interactive dashboards for director, quality manager, marketing, client, and machine learning views.
- Created DAX measures for satisfaction, dissatisfaction, positive ratings, food satisfaction, service satisfaction, and offer-preference adequacy.
- Analyzed customer behavior by age, budget, marital status, drink level, smoker status, preferred cuisine, and transportation method.
- Integrated Python visuals using pandas, scikit-learn, matplotlib, and seaborn for feature importance and rating prediction.
Power BIDAXPythonPandasScikit-learnSeabornData Visualization
ML • XAI
Breast Cancer Prediction Platform
End-to-end ML platform for breast cancer prediction developed using CRISP-DM methodology with model interpretability via SHAP, FastAPI backend, and interactive Next.js frontend.
- ML Models: Logistic Regression, SVM, Random Forest, MLP, XGBoost
- Explainable AI (XAI) using SHAP for prediction interpretation
- FastAPI backend with inference API
- Interactive Next.js web interface
Pythonscikit-learnXGBoostSHAPFastAPINext.js
AI + Web
Agri-Hope Marketplace
Full-stack marketplace for small farmers with an integrated AI assistant.
Next.jsNestJSMongoDB
Experience
Learning by building, mentoring and shipping.
Timeline
Highlights from my journey.
Data Science & AI Engineering Student
Coursework and projects in ML, Big Data, cloud, full-stack development, and software architecture.
IEEE Student Branch
Attended workshops on Data Science, AI, and Web Development, and worked on many projects and events as a team member.
ML & MLOps Side Projects
Built pipelines with proper experimentation, evaluation, and deployment: versioning, metrics, and model reproducibility.
What I'm looking for
Internships • collaborations • research.
I'm interested in internships and collaborations where I can:
- Design and train ML models on real-world datasets.
- Work with data platforms, pipelines, and MLOps tools.
- Build AI-powered features inside modern web apps.
- Contribute to open-source or research-oriented work.
If your team works at the intersection of AI, product, and impact, I'd love to talk.
Contact
Let's build thoughtful AI together.
GitHub
github.com/natej-ghodbane
LinkedIn
linkedin.com/in/natejghodbane
I usually reply in 24-48 hours.
Feel free to share your idea, team, or opportunity.