How amritkaur15/house_price_predictor works
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 House price notebook
Components 9
Local workspace
A small, notebook-driven Python ML project run interactively in Jupyter.
- House price notebook · Main orchestrator for the entire workflow and user interaction surface.
house_price_predictor.ipynb - House data · Training data and any notebook-loaded inputs used for analysis and modeling.
- Prep flow · Cleaning, type handling, missing-value treatment, and feature engineering.
- Train split · Partitioning data for validation and model assessment inside the notebook.
- Sklearn model · Predictive model or pipeline trained on prepared house-price features.
- Evaluate · Validation, scoring, and comparison of model outputs within the notebook.
- Plots · Exploratory charts and result visualizations rendered in notebook outputs.
- Price output · Final predicted house price values inspected or exported from the notebook.
- Python venv · Manually managed Python 3.11 virtual environment used to run the notebook.
Connections 11
- 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.