# amrit-sarker/titanic-logistic-regression architecture

> Architecture diagram of the GitHub repository amrit-sarker/titanic-logistic-regression, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/amrit-sarker/titanic-logistic-regression
- Repository: https://github.com/amrit-sarker/titanic-logistic-regression

## Overview

1. **Repository shape**
   - This is a small, notebook-driven machine learning project.
   - The main working artifact is `titanic_logistic_regression.ipynb`; `submission.csv` is the generated prediction output for Kaggle.

2. **Primary purpose**
   - The repo implements a binary classification baseline for the Kaggle Titanic competition.
   - The system predicts `Survived` for each passenger from tabular passenger attributes.

3. **External data boundary**
   - Input data comes from the Kaggle Titanic dataset, not from an internal service or database.
   - The dataset includes fields such as `Pclass`, `Sex`, `Age`, `SibSp`, `Parch`, `Fare`, and `Embarked`.

4. **Data ingestion and preprocessing layer**
   - The notebook likely loads the Titanic CSV data, then cleans and prepares it for modeling.
   - Missing values are handled as part of preprocessing, specifically for `Age` and `Embarked`.

5. **Feature transformation layer**
   - Categorical variables are converted into model-ready numeric features using one-hot encoding.
   - Feature selection is used to keep the representation interpretable and suitable for a linear baseline.

6. **Modeling subsystem**
   - The core model is scikit-learn Logistic Regression.
   - This is the central learning component: it consumes the processed feature matrix and outputs survival probabilities / class predictions.

7. **Training and validation flow**
   - The notebook uses a manual train-validation split rather than a more elaborate orchestration layer.
   - Model performance is measured with accuracy during local evaluation.

8. **Inference and output generation**
   - After training, the model is applied to the test split from the Kaggle competition data.
   - Predictions are written into `submission.csv`, which is the handoff artifact for Kaggle scoring.

9. **Performance feedback loop**
   - The reported public leaderboard score is 0.77033, which serves as the external evaluation signal.
   - There is no evidence of a deployed API or online serving layer; the feedback loop is notebook → submission file → Kaggle leaderboard.

10. **Technology stack and runtime boundary**
   - The implementation is Python-based, using Pandas, NumPy, and scikit-learn inside Jupyter Notebook.
   - The architecture is a single offline analytics workflow rather than a multi-service application.

11. **Key architectural characteristic**
   - The project’s main boundary is between raw tabular data and a cleaned feature matrix suitable for linear classification.
   - Most of the value is in preprocessing and feature representation, not in complex infrastructure.

12. **Diagramming hint**
   - A clear architecture diagram would show: Kaggle dataset input → notebook preprocessing/encoding → logistic regression training/validation → prediction generation → `submission.csv` output.

Main entry points: [`submission.csv`](https://github.com/amrit-sarker/titanic-logistic-regression/blob/main/submission.csv) (submission.csv), [`titanic_logistic_regression.ipynb`](https://github.com/amrit-sarker/titanic-logistic-regression/blob/main/titanic_logistic_regression.ipynb) (Titanic notebook)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_workflow["Notebook workflow"]
  node_notebook["Titanic notebook<br/>jupyter workflow"]
  node_load_data["Load CSVs<br/>ingestion"]
  node_clean_data["Clean data<br/>preprocessing"]
  node_encode_features["Encode features<br/>feature engineering"]
  node_select_features["Select features<br/>feature selection"]
  node_split_eval["Train split<br/>validation"]
  node_logreg(("Logistic regression<br/>sklearn model"))
  node_predict["Predict survivors<br/>inference"]
end

node_kaggle_data[("Kaggle data<br/>external dataset")]
node_submission["submission.csv<br/>output file<br/>[submission.csv]"]
node_leaderboard(("Kaggle score<br/>external feedback"))

node_kaggle_data -->|"raw tables"| node_load_data
node_notebook -->|"orchestrates"| node_load_data
node_load_data -->|"clean"| node_clean_data
node_clean_data -->|"transform"| node_encode_features
node_encode_features -->|"refine"| node_select_features
node_select_features -->|"split"| node_split_eval
node_split_eval -->|"fit"| node_logreg
node_logreg -->|"score"| node_split_eval
node_select_features -->|"features"| node_logreg
node_logreg -->|"infer"| node_predict
node_predict -->|"write"| node_submission
node_submission -->|"submit"| node_leaderboard

click node_notebook "https://github.com/amrit-sarker/titanic-logistic-regression/blob/main/titanic_logistic_regression.ipynb"
click node_submission "https://github.com/amrit-sarker/titanic-logistic-regression/blob/main/submission.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_notebook,node_load_data,node_clean_data,node_encode_features,node_select_features,node_split_eval,node_logreg,node_predict toneBlue
class node_kaggle_data,node_submission,node_leaderboard toneNeutral
```

## Components

### Notebook workflow

Offline Kaggle baseline implemented in a single Jupyter notebook.

- [Titanic notebook](https://github.com/amrit-sarker/titanic-logistic-regression/blob/main/titanic_logistic_regression.ipynb): jupyter workflow · Main notebook that loads data, prepares features, trains the model, and writes predictions. · `titanic_logistic_regression.ipynb`
- **Load CSVs**: ingestion · Reads the Titanic training and test tables into the notebook environment.
- **Clean data**: preprocessing · Handles missing values and basic tabular cleanup, including Age and Embarked.
- **Encode features**: feature engineering · Converts categorical columns into numeric model inputs with one-hot encoding.
- **Select features**: feature selection · Keeps a compact, interpretable feature set for the linear baseline.
- **Train split**: validation · Uses a manual train-validation split and accuracy for local evaluation.
- **Logistic regression**: sklearn model · Core classifier that learns to predict survival from the processed feature matrix.
- **Predict survivors**: inference · Applies the trained model to the competition test set to generate class predictions.

### Other components

- **Kaggle data**: external dataset · Titanic competition train/test tabular data used as the raw input boundary.
- [submission.csv](https://github.com/amrit-sarker/titanic-logistic-regression/blob/main/submission.csv): output file · Kaggle submission artifact containing predicted survival labels. · `submission.csv`
- **Kaggle score**: external feedback · Public leaderboard result used as the external evaluation signal.

## Connections

- Kaggle data → Load CSVs: raw tables · Competition data is loaded into the notebook.
- Titanic notebook → Load CSVs: orchestrates · The notebook drives ingestion and all later steps.
- Load CSVs → Clean data: clean · Loaded tables are cleaned and missing values are handled.
- Clean data → Encode features: transform · Categorical and numeric columns are prepared for modeling.
- Encode features → Select features: refine · Encoded representation is reduced to the chosen baseline features.
- Select features → Train split: split · Feature matrix is divided for local validation.
- Train split → Logistic regression: fit · Training data is used to fit the classifier.
- Logistic regression → Train split: score · Validation accuracy is computed from model predictions.
- Select features → Logistic regression: features · Processed features are fed into the logistic regression model.
- Logistic regression → Predict survivors: infer · The trained model produces predictions for the test set.
- Predict survivors → submission.csv: write · Predictions are written to the submission CSV file.
- submission.csv → Kaggle score: submit · The generated file is uploaded to Kaggle for scoring.