# amritkaur15/house_price_predictor architecture

> Architecture diagram of the GitHub repository amritkaur15/house_price_predictor, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-20). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/amritkaur15/house_price_predictor
- Repository: https://github.com/amritkaur15/house_price_predictor

## Overview

This repository is a very small, notebook-driven machine learning project centered on `house_price_predictor.ipynb`. There is no application server, package structure, or deployment layer visible in the repo.

1. **Primary artifact**
   - The core system is a single Jupyter notebook.
   - That notebook likely contains the full workflow: data loading, cleaning, feature preparation, model training, evaluation, and prediction.

2. **Runtime model**
   - Execution is local and interactive through Jupyter Notebook.
   - The repo expects a Python 3.11 virtual environment managed manually with `venv`.

3. **Main technology stack**
   - `pandas` and `numpy` for tabular data handling and numerical operations.
   - `scikit-learn` for the predictive modeling pipeline.
   - `matplotlib` for plots and exploratory visualization.
   - `python-dateutil` is included, suggesting date parsing or timestamp feature handling may be part of the notebook.

4. **Likely data flow**
   - Input data is loaded into the notebook from local files or inline notebook cells.
   - Data is transformed in-memory with pandas/numpy.
   - Features are passed into a scikit-learn model.
   - Results are visualized and inspected directly in the notebook output cells.

5. **Modeling boundary**
   - The only clear architectural boundary is between the notebook’s data preparation logic and the scikit-learn model layer.
   - Any train/test split, validation, and prediction steps are likely performed inside the same notebook rather than in separate services or modules.

6. **User interaction**
   - The notebook is the user interface.
   - A person runs cells manually, observes plots/metrics, and iterates on preprocessing or model choice interactively.

7. **No external infrastructure**
   - There is no evidence of databases, APIs, message queues, cloud services, or containers.
   - The project appears entirely self-contained for local experimentation.

8. **Repository structure implications**
   - With only one notebook plus metadata files, this is closer to a prototype or analysis workspace than a production system.
   - Any architecture diagram should show a single notebook as the orchestrator, not separate backend/frontend components.

9. **Documentation mismatch**
   - The README instructs users to open `sentiment_classifier.ipynb`, but the repository contains `house_price_predictor.ipynb`.
   - That suggests the README is stale or copied from another project, and the actual notebook name should be treated as the source of truth.

10. **What to diagram**
   - Show one notebook node coordinating:
     - data ingestion
     - preprocessing/feature engineering
     - model training with scikit-learn
     - evaluation/visualization
     - predicted house price output
   - Keep the diagram centered on a local notebook workflow rather than a distributed architecture.

Main entry points: [`house_price_predictor.ipynb`](https://github.com/amritkaur15/house_price_predictor/blob/main/house_price_predictor.ipynb) (House price notebook)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_workspace["Local workspace"]
  node_notebook["House price notebook<br/>jupyter notebook"]
  node_input_data[("House data<br/>local data")]
  node_prep["Prep flow<br/>pandas/numpy prep"]
  node_split["Train split<br/>dataset split"]
  node_model{{"Sklearn model<br/>scikit-learn model"}}
  node_eval["Evaluate<br/>metrics check"]
  node_viz(("Plots<br/>matplotlib viz"))
  node_predict["Price output<br/>prediction result"]
  node_runtime[("Python venv<br/>local runtime")]
end

node_runtime -->|"runs in"| node_notebook
node_notebook -->|"loads"| node_input_data
node_notebook -->|"prepares"| node_prep
node_input_data -->|"feeds"| node_prep
node_prep -->|"splits"| node_split
node_split -->|"trains"| node_model
node_model -->|"scores"| node_eval
node_eval -->|"plots"| node_viz
node_model -->|"outputs"| node_predict
node_notebook -->|"renders"| node_viz
node_notebook -->|"presents"| node_predict

click node_notebook "https://github.com/amritkaur15/house_price_predictor/blob/main/house_price_predictor.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_notebook,node_input_data,node_prep,node_split,node_model,node_eval,node_viz,node_predict,node_runtime toneBlue
```

## Components

### Local workspace

A small, notebook-driven Python ML project run interactively in Jupyter.

- [House price notebook](https://github.com/amritkaur15/house_price_predictor/blob/main/house_price_predictor.ipynb): jupyter notebook · Main orchestrator for the entire workflow and user interaction surface. · `house_price_predictor.ipynb`
- **House data**: local data · Training data and any notebook-loaded inputs used for analysis and modeling.
- **Prep flow**: pandas/numpy prep · Cleaning, type handling, missing-value treatment, and feature engineering.
- **Train split**: dataset split · Partitioning data for validation and model assessment inside the notebook.
- **Sklearn model**: scikit-learn model · Predictive model or pipeline trained on prepared house-price features.
- **Evaluate**: metrics check · Validation, scoring, and comparison of model outputs within the notebook.
- **Plots**: matplotlib viz · Exploratory charts and result visualizations rendered in notebook outputs.
- **Price output**: prediction result · Final predicted house price values inspected or exported from the notebook.
- **Python venv**: local runtime · Manually managed Python 3.11 virtual environment used to run the notebook.

## Connections

- Python venv → House price notebook: runs in · The notebook executes inside the local Python environment.
- House price notebook → House data: loads · Notebook cells read local or inline data into memory.
- House price notebook → Prep flow: prepares · The notebook coordinates cleaning and feature engineering steps.
- House data → Prep flow: feeds · Raw data is transformed into model-ready features.
- Prep flow → Train split: splits · Prepared data is divided for training and validation.
- Train split → Sklearn model: trains · Training subset is used to fit the scikit-learn model.
- Sklearn model → Evaluate: scores · Model predictions are evaluated against held-out data.
- Evaluate → Plots: plots · Metrics and comparisons are visualized for inspection.
- Sklearn model → Price output: outputs · The trained model produces house price predictions.
- House price notebook → Plots: renders · Notebook displays exploratory and result plots directly to the user.
- House price notebook → Price output: presents · Final predictions are reviewed interactively in notebook cells.