# surya2324/house-price-prediction-using-machine-learning architecture

> Architecture diagram of the GitHub repository surya2324/house-price-prediction-using-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/surya2324/house-price-prediction-using-machine-learning
- Repository: https://github.com/surya2324/house-price-prediction-using-machine-learning

## Overview

This is a Bengaluru house-price prediction project. A user enters property details in the Streamlit app, which uses the cleaned dataset to offer locations and a saved model to predict and display a price. The README also documents preprocessing, exploratory analysis, multiple-model training, and evaluation in the notebook; notebook internals were not sampled, so those capabilities are represented without inferring their detailed wiring.

Main entry points: [`app.py`](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/app.py) (Streamlit predictor), [`Machine_Learning_Project-House_Price_Prediction.ipynb`](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Machine_Learning_Project-House_Price_Prediction.ipynb) (Analysis and model training), [`House_prediction_model.pkl`](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/House_prediction_model.pkl) (Saved price model), [`Cleaned_data.csv`](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Cleaned_data.csv) (Cleaned housing data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_app["Prediction app"]
  node_streamlit["Streamlit predictor<br/>[app.py]"]
end

subgraph group_ml["ML workflow"]
  node_model["Saved price model"]
  node_notebook["Analysis and model training"]
end

subgraph group_data["Project data"]
  node_cleaned["Cleaned housing data<br/>[Cleaned_data.csv]"]
  node_raw["Bengaluru housing data"]
end

node_user(("Home seeker"))

node_user -->|"enters features"| node_streamlit
node_streamlit -->|"reads locations"| node_cleaned
node_streamlit -->|"loads model"| node_model
node_streamlit -->|"predicts price"| node_model
node_streamlit -->|"shows estimate"| node_user
node_notebook -->|"analyzes data"| node_raw
node_notebook -->|"prepares data"| node_cleaned
node_notebook -->|"trains and evaluates"| node_model

click node_streamlit "https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/app.py"
click node_cleaned "https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Cleaned_data.csv"
click node_model "https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/House_prediction_model.pkl"
click node_notebook "https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Machine_Learning_Project-House_Price_Prediction.ipynb"
click node_raw "https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Bengaluru_House_Data.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_streamlit toneBlue
class node_model,node_notebook toneAmber
class node_cleaned,node_raw toneMint
class node_user toneIndigo
```

## Components

### Prediction app

- [Streamlit predictor](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/app.py): `app.py`

### ML workflow

- [Saved price model](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/House_prediction_model.pkl): `House_prediction_model.pkl`
- [Analysis and model training](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Machine_Learning_Project-House_Price_Prediction.ipynb): `Machine_Learning_Project-House_Price_Prediction.ipynb`

### Project data

- [Cleaned housing data](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Cleaned_data.csv): `Cleaned_data.csv`
- [Bengaluru housing data](https://github.com/surya2324/house-price-prediction-using-machine-learning/blob/main/Bengaluru_House_Data.csv): `Bengaluru_House_Data.csv`

### Other components

- **Home seeker**

## Connections

- Home seeker → Streamlit predictor: enters features
- Streamlit predictor → Cleaned housing data: reads locations
- Streamlit predictor → Saved price model: loads model
- Streamlit predictor → Saved price model: predicts price
- Streamlit predictor → Home seeker: shows estimate
- Analysis and model training → Bengaluru housing data: analyzes data
- Analysis and model training → Cleaned housing data: prepares data
- Analysis and model training → Saved price model: trains and evaluates