# jaydhandhukiya14/nyc_house_types-prediction architecture

> Architecture diagram of the GitHub repository jaydhandhukiya14/nyc_house_types-prediction, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-08-01). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/jaydhandhukiya14/nyc_house_types-prediction
- Repository: https://github.com/jaydhandhukiya14/nyc_house_types-prediction

## Overview

#### Purpose and Entry Points
Listing-Forecast is a small NYC Airbnb listing prediction project. The runnable backend entry point is `main.py`; its prediction model is persisted at `model/Model_Pipeline.pkl`. A separate static browser interface starts from `Frontend/index.html` and loads client behavior from `Frontend/script.js`.

#### Model Training Artifact
The repository ships a prebuilt serialized model pipeline rather than training at runtime. `model/Model_Pipeline.pkl` is the core inference dependency, likely encapsulating preprocessing and the estimator. Training/exploration appears to be represented by `notebook/nyc_airbnb_room_type.ipynb`, backed by `dataset/AB_NYC_2019.csv`.

#### Backend Inference Layer
`main.py` is the application control boundary: it should receive listing attributes, load/use the model pipeline, run inference, and return a forecast or classification result. It is the integration point between request-facing behavior and the offline-generated model artifact.

#### Frontend Client Layer
The frontend is a standalone static UI:
- `Frontend/index.html` defines the page and input surface.
- `Frontend/script.js` collects user input and invokes prediction behavior.
- `Frontend/style.css` supplies presentation.

Its key boundary is communication with the backend in `main.py`; deployment must make their expected endpoint/origin available together or configure cross-origin access.

#### Data Flow
1. Historical Airbnb records originate in `dataset/AB_NYC_2019.csv`.
2. The notebook in `notebook/nyc_airbnb_room_type.ipynb` explores/trains the model workflow.
3. The trained pipeline is saved as `model/Model_Pipeline.pkl`.
4. `main.py` loads the pipeline and applies it to incoming listing data.
5. The frontend renders user inputs and displays the returned prediction.

#### Runtime and Dependencies
Python is the model-serving runtime, pinned/selected through `.python-version` and dependency definitions in `requirements.txt`. Browser execution requires only static HTML/CSS/JavaScript assets under `Frontend/`. The only identified external data source is the bundled NYC Airbnb CSV; no cloud infrastructure, database, or third-party API is represented in the supplied tree.

Main entry points: [`main.py`](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/main.py) (Inference entry point), [`Frontend/script.js`](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/script.js) (Prediction client), [`Frontend/index.html`](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/index.html) (Listing input page), [`notebook/nyc_airbnb_room_type.ipynb`](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/notebook/nyc_airbnb_room_type.ipynb) (Training notebook)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_offline["Offline model workflow"]
  node_dataset[("NYC Airbnb records<br/>CSV dataset<br/>[AB_NYC_2019.csv]")]
  node_notebook["Training notebook"]
  node_model{{"Serialized model pipeline<br/>pickle model<br/>[Model_Pipeline.pkl]"}}
end

subgraph group_backend["Python inference"]
  node_main["Inference entry point<br/>Python application<br/>[main.py]"]
  node_python_version["Python version<br/>runtime selector<br/>[.python-version]"]
  node_requirements["Python dependencies<br/>dependency manifest<br/>[requirements.txt]"]
end

subgraph group_frontend["Static browser client"]
  node_html["Listing input page<br/>HTML page<br/>[index.html]"]
  node_script["Prediction client<br/>browser JavaScript<br/>[script.js]"]
  node_styles["Page styles<br/>CSS stylesheet<br/>[style.css]"]
end

node_user(("User"))

node_dataset -->|"explored and used for training"| node_notebook
node_notebook -->|"produces serialized pipeline"| node_model
node_model -->|"loaded for inference"| node_main
node_python_version -.->|"selects runtime"| node_main
node_requirements -.->|"provides dependencies"| node_main
node_user -->|"opens"| node_html
node_html -->|"loads"| node_script
node_styles -->|"styles"| node_html
node_script -->|"sends listing attributes"| node_main
node_main -->|"returns prediction"| node_script
node_script -->|"renders result"| node_html

click node_dataset "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/dataset/AB_NYC_2019.csv"
click node_notebook "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/notebook/nyc_airbnb_room_type.ipynb"
click node_model "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/model/Model_Pipeline.pkl"
click node_main "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/main.py"
click node_python_version "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/.python-version"
click node_requirements "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/requirements.txt"
click node_html "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/index.html"
click node_script "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/script.js"
click node_styles "https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/style.css"

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_dataset,node_notebook,node_model toneBlue
class node_main,node_python_version,node_requirements toneAmber
class node_html,node_script,node_styles toneMint
class node_user toneNeutral
```

## Components

### Offline model workflow

- [NYC Airbnb records](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/dataset/AB_NYC_2019.csv): CSV dataset · `dataset/AB_NYC_2019.csv`
- [Training notebook](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/notebook/nyc_airbnb_room_type.ipynb): notebook · Explores and trains the listing model workflow. · `notebook/nyc_airbnb_room_type.ipynb`
- [Serialized model pipeline](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/model/Model_Pipeline.pkl): pickle model · Persisted preprocessing and prediction pipeline. · `model/Model_Pipeline.pkl`

### Python inference

- [Inference entry point](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/main.py): Python application · Receives listing attributes and returns predictions. · `main.py`
- [Python version](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/.python-version): runtime selector · `.python-version`
- [Python dependencies](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/requirements.txt): dependency manifest · `requirements.txt`

### Static browser client

- [Listing input page](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/index.html): HTML page · Defines the browser input and result surface. · `Frontend/index.html`
- [Prediction client](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/script.js): browser JavaScript · Collects inputs, invokes prediction, and renders results. · `Frontend/script.js`
- [Page styles](https://github.com/jaydhandhukiya14/nyc_house_types-prediction/blob/main/Frontend/style.css): CSS stylesheet · `Frontend/style.css`

### Other components

- **User**: browser user

## Connections

- NYC Airbnb records → Training notebook: explored and used for training
- Training notebook → Serialized model pipeline: produces serialized pipeline
- Serialized model pipeline → Inference entry point: loaded for inference
- Python version → Inference entry point: selects runtime
- Python dependencies → Inference entry point: provides dependencies
- User → Listing input page: opens
- Listing input page → Prediction client: loads
- Page styles → Listing input page: styles
- Prediction client → Inference entry point: sends listing attributes
- Inference entry point → Prediction client: returns prediction
- Prediction client → Listing input page: renders result