# dasuna/its-2140---machine-learning architecture

> Architecture diagram of the GitHub repository dasuna/its-2140---machine-learning, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-26). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/dasuna/its-2140---machine-learning
- Repository: https://github.com/dasuna/its-2140---machine-learning

## Overview

This repository is a collection of machine-learning lab sessions, not a packaged runtime application. The principal user workflow is for a learner to work through notebook-based exercises, from regression and classification fundamentals to clustering, model inference, deployment, and experiment tracking. Notebook titles identify these capabilities, but no notebook source was available to confirm execution order, dataset usage, or internal calls; the graph therefore shows learner access rather than inferred runtime flows.

Main entry points: [`Week 2/advertising_sales_prediction.ipynb`](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%202/advertising_sales_prediction.ipynb) (Advertising Regression), [`Week 3/multiple_linear_regression_SGD.ipynb`](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%203/multiple_linear_regression_SGD.ipynb) (Multiple Regression SGD), [`Week 4/logistic_regression_new.ipynb`](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%204/logistic_regression_new.ipynb) (Logistic Regression), [`Week 6/softmax_regression.ipynb`](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%206/softmax_regression.ipynb) (Softmax Regression)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_foundations["Modeling Fundamentals"]
  node_advertising["Advertising Regression"]
  node_multiple_regression["Multiple Regression SGD"]
  node_logistic_regression["Logistic Regression"]
  node_softmax_regression["Softmax Regression"]
end

subgraph group_unsupervised["Unsupervised Learning"]
  node_kmeans["K-Means Clustering"]
end

subgraph group_lifecycle["Model Lifecycle"]
  node_inferences["Model Inference"]
  node_artifacts["Training and Artifacts"]
  node_sentiment_deployment["Sentiment Deployment"]
end

subgraph group_experiments["Experimentation"]
  node_hyperparameter_tuning["Hyperparameter Tuning"]
  node_logistic_tracking["Logistic Tracking"]
  node_mlflow_tracking["MLflow Tracking"]
end

node_learner(("Learner"))
node_advertising_data["Advertising Data<br/>[advertising.csv]"]
node_reviews_week4["Clothing Reviews Data"]
node_reviews_week7["Clothing Reviews Data"]

node_learner -->|"studies"| node_advertising
node_learner -->|"studies"| node_multiple_regression
node_learner -->|"studies"| node_logistic_regression
node_learner -->|"studies"| node_softmax_regression
node_learner -->|"studies"| node_kmeans
node_learner -->|"studies"| node_inferences
node_learner -->|"studies"| node_artifacts
node_learner -->|"studies"| node_sentiment_deployment
node_learner -->|"studies"| node_hyperparameter_tuning
node_learner -->|"studies"| node_logistic_tracking
node_learner -->|"studies"| node_mlflow_tracking

click node_advertising "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%202/advertising_sales_prediction.ipynb"
click node_multiple_regression "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%203/multiple_linear_regression_SGD.ipynb"
click node_logistic_regression "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%204/logistic_regression_new.ipynb"
click node_softmax_regression "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%206/softmax_regression.ipynb"
click node_kmeans "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%205/k-means-clustering.ipynb"
click node_inferences "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%207/get_inferences.ipynb"
click node_artifacts "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%207/model_training_and_artifacts_saving.ipynb"
click node_sentiment_deployment "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%208/sentiment-analysis_sklearn-pipeline_deployment.ipynb"
click node_hyperparameter_tuning "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%209/hyperparameter_tuning_experiment.ipynb"
click node_logistic_tracking "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%209/logistic_regression_tracking.ipynb"
click node_mlflow_tracking "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%209/ml_flow_tracking.ipynb"
click node_advertising_data "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%202/advertising.csv"
click node_reviews_week4 "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%204/womens_clothing_ecommerce_reviews.csv"
click node_reviews_week7 "https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%207/womens_clothing_ecommerce_reviews.csv"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_advertising,node_multiple_regression,node_logistic_regression,node_softmax_regression,node_reviews_week4,node_reviews_week7 toneBlue
class node_kmeans toneAmber
class node_inferences,node_artifacts,node_sentiment_deployment toneMint
class node_hyperparameter_tuning,node_logistic_tracking,node_mlflow_tracking toneRose
class node_learner toneIndigo
class node_advertising_data toneTeal
```

## Components

### Modeling Fundamentals

- [Advertising Regression](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%202/advertising_sales_prediction.ipynb): `Week 2/advertising_sales_prediction.ipynb`
- [Multiple Regression SGD](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%203/multiple_linear_regression_SGD.ipynb): `Week 3/multiple_linear_regression_SGD.ipynb`
- [Logistic Regression](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%204/logistic_regression_new.ipynb): `Week 4/logistic_regression_new.ipynb`
- [Softmax Regression](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%206/softmax_regression.ipynb): `Week 6/softmax_regression.ipynb`

### Unsupervised Learning

- [K-Means Clustering](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%205/k-means-clustering.ipynb): `Week 5/k-means-clustering.ipynb`

### Model Lifecycle

- [Model Inference](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%207/get_inferences.ipynb): `Week 7/get_inferences.ipynb`
- [Training and Artifacts](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%207/model_training_and_artifacts_saving.ipynb): `Week 7/model_training_and_artifacts_saving.ipynb`
- [Sentiment Deployment](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%208/sentiment-analysis_sklearn-pipeline_deployment.ipynb): `Week 8/sentiment-analysis_sklearn-pipeline_deployment.ipynb`

### Experimentation

- [Hyperparameter Tuning](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%209/hyperparameter_tuning_experiment.ipynb): `Week 9/hyperparameter_tuning_experiment.ipynb`
- [Logistic Tracking](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%209/logistic_regression_tracking.ipynb): `Week 9/logistic_regression_tracking.ipynb`
- [MLflow Tracking](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%209/ml_flow_tracking.ipynb): `Week 9/ml_flow_tracking.ipynb`

### Other components

- **Learner**
- [Advertising Data](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%202/advertising.csv): `Week 2/advertising.csv`
- [Clothing Reviews Data](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%204/womens_clothing_ecommerce_reviews.csv): `Week 4/womens_clothing_ecommerce_reviews.csv`
- [Clothing Reviews Data](https://github.com/dasuna/its-2140---machine-learning/blob/main/Week%207/womens_clothing_ecommerce_reviews.csv): `Week 7/womens_clothing_ecommerce_reviews.csv`

## Connections

- Learner → Advertising Regression: studies
- Learner → Multiple Regression SGD: studies
- Learner → Logistic Regression: studies
- Learner → Softmax Regression: studies
- Learner → K-Means Clustering: studies
- Learner → Model Inference: studies
- Learner → Training and Artifacts: studies
- Learner → Sentiment Deployment: studies
- Learner → Hyperparameter Tuning: studies
- Learner → Logistic Tracking: studies
- Learner → MLflow Tracking: studies