| Key | Value |
|---|---|
| Course Codes | BBT 4206 |
| Course Names | BBT 4206: Business Intelligence II (Week 4-6 of 13) |
| Semester | January to April 2026 |
| Lecturer | Allan Omondi |
| Contact | aomondi@strathmore.edu |
| Note | The lecture contains both theory and practice. This notebook forms part of the practice. It is intended for educational purposes only. Recommended citation: BibTex |
.
├── Docker-Compose.yaml
├── LICENSE
├── Procfile
├── README.md
├── RecommendedCitation.bib
├── api.py
├── app_server_reverse_proxy_server_setup.md
├── assets
│ └── images
├── cleanup_instructions.md
├── container-volumes
│ ├── gunicorn
│ ├── nginx
│ │ └── nginx.conf
│ └── ubuntu
├── docker-compose-dev.yaml
├── docker-compose-prod.yaml
├── env.example
├── frontend
│ ├── Proxies.png
│ ├── RequestFlow.jpg
│ ├── RequestFlow.png
│ ├── api_consumer.py
│ ├── api_test_DT_classifier.html
│ ├── api_test_DT_regressor.html
│ └── index.html
├── huggingface-spaces-using-gradio
│ ├── app.py
│ └── requirements.txt
├── images
│ ├── Dockerfile.flask-gunicorn-app
│ ├── Dockerfile.nginx
│ ├── OLD_Dockerfile.flask-gunicorn-app
│ └── ubuntu
│ ├── Dockerfile.ubuntu
│ └── entrypoint.sh
├── lab_submission_instructions.md
├── model
│ ├── decisiontree_classifier_baseline.pkl
│ ├── decisiontree_regressor_optimum.pkl
│ ├── knn_classifier_optimum.pkl
│ ├── label_encoders_1b.pkl
│ ├── label_encoders_2.pkl
│ ├── label_encoders_4.pkl
│ ├── label_encoders_5.pkl
│ ├── naive_Bayes_classifier_optimum.pkl
│ ├── onehot_encoder_3.pkl
│ ├── random_forest_classifier_optimum.pkl
│ ├── scaler_4.pkl
│ ├── scaler_5.pkl
│ └── support_vector_classifier_optimum.pkl
├── publicly_serving_the_model_for_validation_by_domain_experts.md
├── requirements
│ ├── base.txt
│ ├── colab.txt
│ ├── constraints.txt
│ ├── dev.inferred.txt
│ ├── dev.lock.txt
│ ├── dev.txt
│ └── prod.txt
├── rules
├── runtime.txt
├── setup_instructions.md
└── streamlit-sharing-using-streamlit
├── app.py
└── requirements.txt
15 directories, 52 files
Refer to the files below for more details: