# raianrashidrimon/titanic-survival-prediction architecture

> Architecture diagram of the GitHub repository raianrashidrimon/titanic-survival-prediction, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/raianrashidrimon/titanic-survival-prediction
- Repository: https://github.com/raianrashidrimon/titanic-survival-prediction

## Overview

1. **Project shape**
   - This is a small, notebook-driven machine learning repo.
   - The only executable artifact is `Prediction Code.ipynb`; `titanic.csv` is the local dataset input.
   - There is no separate application server, API, or deployment layer.

2. **Primary data source boundary**
   - `titanic.csv` is the external data boundary for the project.
   - The notebook reads this file directly and uses it as the full source of truth for training and evaluation.

3. **Notebook as the orchestration layer**
   - The Jupyter notebook contains the entire workflow end-to-end.
   - It likely handles importing libraries, loading data, preprocessing, feature selection, model training, evaluation, and result visualization in one place.

4. **Data preprocessing subsystem**
   - The README indicates preprocessing includes:
     - mapping categorical text fields like `Sex` and `Embarked` to numeric values,
     - filling missing values such as `Age` with the mean,
     - selecting a subset of relevant features.
   - This is the main transformation boundary between raw CSV data and model-ready tabular input.

5. **Feature/target split**
   - The model is trained on selected passenger features and predicts the survival target.
   - Architecturally, this separates the input feature matrix from the label vector, which is important for understanding the ML pipeline.

6. **Model training subsystem**
   - The core estimator is a Random Forest classifier.
   - This is the learning component that consumes the preprocessed training data and produces a fitted survival-prediction model.

7. **Evaluation subsystem**
   - The README references “evaluation metrics” and a performance image.
   - The notebook likely computes standard classification metrics on held-out data, giving a separate evaluation step after training rather than judging on the training set itself.

8. **Visualization/output boundary**
   - Results are presented through notebook outputs and an included performance graphic.
   - The project’s outputs are therefore exploratory and report-oriented, not packaged as a runtime service.

9. **Dependencies and runtime**
   - The runtime is Python 3.7+ with `pandas`, `matplotlib`, `scikit-learn`, and `seaborn`.
   - These libraries indicate a standard data science workflow: tabular manipulation, plotting, and classical ML.

10. **Repository structure implication**
    - Because there is no modular source tree, the architecture is intentionally flat and educational.
    - The notebook is the system boundary, and the CSV is the only persistent input artifact.

11. **What is not present**
    - No database, message queue, web frontend, backend API, or model serving layer.
    - No CI/CD or containerization artifacts are shown, so the repo is focused on offline experimentation rather than production deployment.

12. **How to draw the architecture**
    - Show `titanic.csv` feeding into the notebook.
    - Inside the notebook, depict preprocessing leading to feature engineering, then Random Forest training, then evaluation/visualization.
    - Keep all computation inside one notebook boundary, with outputs rendered as plots/metrics rather than external service responses.

Main entry points: [`Prediction Code.ipynb`](https://github.com/raianrashidrimon/titanic-survival-prediction/blob/HEAD/Prediction%20Code.ipynb) (Prediction Code), [`titanic.csv`](https://github.com/raianrashidrimon/titanic-survival-prediction/blob/main/titanic.csv) (titanic.csv)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data Boundary"]
  node_titanic_csv[("titanic.csv<br/>dataset<br/>[titanic.csv]")]
end

subgraph group_workflow["Notebook Workflow"]
  node_notebook["Prediction Code<br/>jupyter notebook"]
  node_load_data["Load Data<br/>ingest"]
  node_preprocess["Preprocess<br/>transform"]
  node_split["Feature Split<br/>feature target split"]
  node_train_rf{{"Random Forest<br/>classifier"}}
  node_evaluate["Evaluate<br/>metrics"]
  node_visualize(("Visualize<br/>plots"))
end

node_titanic_csv -->|"input"| node_load_data
node_notebook -->|"orchestrates"| node_load_data
node_load_data -->|"clean"| node_preprocess
node_preprocess -->|"prepare"| node_split
node_split -->|"train"| node_train_rf
node_train_rf -->|"predict"| node_evaluate
node_evaluate -->|"report"| node_visualize
node_visualize -.->|"output"| node_notebook

click node_titanic_csv "https://github.com/raianrashidrimon/titanic-survival-prediction/blob/main/titanic.csv"
click node_notebook "https://github.com/raianrashidrimon/titanic-survival-prediction/blob/main/Prediction Code.ipynb"

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_titanic_csv toneBlue
class node_notebook,node_load_data,node_preprocess,node_split,node_train_rf,node_evaluate,node_visualize toneAmber
```

## Components

### Data Boundary

External input dataset consumed directly by the notebook.

- [titanic.csv](https://github.com/raianrashidrimon/titanic-survival-prediction/blob/main/titanic.csv): dataset · Raw passenger records used as the source of truth for training and evaluation. · `titanic.csv`

### Notebook Workflow

End-to-end exploratory ML pipeline contained in the Jupyter notebook.

- [Prediction Code](https://github.com/raianrashidrimon/titanic-survival-prediction/blob/HEAD/Prediction%20Code.ipynb): jupyter notebook · Single executable notebook that orchestrates the full workflow. · `Prediction Code.ipynb`
- **Load Data**: ingest · Reads the CSV into tabular form inside the notebook.
- **Preprocess**: transform · Maps categorical values, fills missing data, and prepares model-ready rows.
- **Feature Split**: feature target split · Separates passenger features from the survival label.
- **Random Forest**: classifier · Fits the survival prediction model on the prepared training data.
- **Evaluate**: metrics · Computes classification performance on held-out data.
- **Visualize**: plots · Renders metrics and performance graphics in notebook output.

## Connections

- titanic.csv → Load Data: input · Dataset is read directly by the notebook.
- Prediction Code → Load Data: orchestrates · Notebook drives data loading and the rest of the pipeline.
- Load Data → Preprocess: clean · Raw table is normalized for modeling.
- Preprocess → Feature Split: prepare · Transformed data is separated into features and target.
- Feature Split → Random Forest: train · Model learns from selected features and survival labels.
- Random Forest → Evaluate: predict · Fitted classifier is assessed on held-out data.
- Evaluate → Visualize: report · Metrics are shown as notebook outputs and figures.
- Visualize → Prediction Code: output · Results remain inside the notebook as exploratory artifacts.