# sahilkavathekar7070-cyber/housing_regressoin_predictionmodel architecture

> Architecture diagram of the GitHub repository sahilkavathekar7070-cyber/housing_regressoin_predictionmodel, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-12). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel
- Repository: https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel

## Overview

This repository implements a small housing-data preparation workflow, not a complete prediction model: its runtime loads a CSV, engineers one feature, splits the data, scales features, and prints resulting shapes. The README gives only the project title, so model training and prediction capabilities are not evidenced. The ingestion code uses a hard-coded Windows path; the dataset node reflects the matching CSV in the tree, but the excerpts do not confirm that this path resolves in the current environment.

Main entry points: [`main.py`](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/main.py) (Workflow entry point), [`src/data_preprocessing.py`](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py) (Train/test split), [`src/data_ingestion.py`](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_ingestion.py) (CSV ingestion), [`data/housing_regression_dataset.csv`](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/data/housing_regression_dataset.csv) (Housing dataset)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_orchestration["Workflow orchestration"]
  node_main["Workflow entry point<br/>[main.py]"]
  node_ingestion["CSV ingestion<br/>[data_ingestion.py]"]
end

subgraph group_preparation["Data preparation"]
  node_feature_engineering["Feature engineering"]
  node_split["Train/test split"]
  node_scaling["Feature scaling"]
end

subgraph group_input["Input data"]
  node_dataset["Housing dataset"]
end

node_sklearn_split["scikit-learn split utility"]
node_sklearn_scaler["scikit-learn RobustScaler"]

node_main -->|"calls"| node_ingestion
node_ingestion -->|"reads CSV"| node_dataset
node_main -->|"passes data"| node_feature_engineering
node_feature_engineering -->|"engineers then splits"| node_split
node_split -->|"calls"| node_sklearn_split
node_split -->|"passes partitions"| node_scaling
node_scaling -->|"fits and transforms"| node_sklearn_scaler
node_main -->|"prints output shapes"| node_scaling

click node_main "https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/main.py"
click node_ingestion "https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_ingestion.py"
click node_dataset "https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/data/housing_regression_dataset.csv"
click node_feature_engineering "https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py"
click node_split "https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py"
click node_scaling "https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py"

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_main,node_ingestion toneBlue
class node_feature_engineering,node_split,node_scaling toneAmber
class node_dataset toneMint
class node_sklearn_split,node_sklearn_scaler toneIndigo
```

## Components

### Workflow orchestration

- [Workflow entry point](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/main.py): `main.py`
- [CSV ingestion](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_ingestion.py): `src/data_ingestion.py`

### Data preparation

- [Feature engineering](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py): `src/data_preprocessing.py`
- [Train/test split](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py): `src/data_preprocessing.py`
- [Feature scaling](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/src/data_preprocessing.py): `src/data_preprocessing.py`

### Input data

- [Housing dataset](https://github.com/sahilkavathekar7070-cyber/housing_regressoin_predictionmodel/blob/main/data/housing_regression_dataset.csv): `data/housing_regression_dataset.csv`

### Other components

- **scikit-learn split utility**
- **scikit-learn RobustScaler**

## Connections

- Workflow entry point → CSV ingestion: calls (evidence: `main.py`)
- CSV ingestion → Housing dataset: reads CSV (evidence: `src/data_ingestion.py`)
- Workflow entry point → Feature engineering: passes data (evidence: `main.py`)
- Feature engineering → Train/test split: engineers then splits (evidence: `src/data_preprocessing.py`)
- Train/test split → scikit-learn split utility: calls (evidence: `src/data_preprocessing.py`)
- Train/test split → Feature scaling: passes partitions (evidence: `src/data_preprocessing.py`)
- Feature scaling → scikit-learn RobustScaler: fits and transforms (evidence: `src/data_preprocessing.py`)
- Workflow entry point → Feature scaling: prints output shapes (evidence: `main.py`)