How sahilkavathekar7070-cyber/housing_regressoin_predictionmodel works
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 Workflow entry point · data_preprocessing.py Train/test split · data_ingestion.py CSV ingestion · housing_regression_dataset.csv Housing dataset
Components 8
Workflow orchestration
- Workflow entry point
main.py - CSV ingestion
src/data_ingestion.py
Data preparation
- Feature engineering
src/data_preprocessing.py - Train/test split
src/data_preprocessing.py - Feature scaling
src/data_preprocessing.py
Input data
- Housing dataset
data/housing_regression_dataset.csv
Other components
- scikit-learn split utility
- scikit-learn RobustScaler
Connections 8
- Workflow entry point → CSV ingestion: calls
main.py - CSV ingestion → Housing dataset: reads CSV
src/data_ingestion.py - Workflow entry point → Feature engineering: passes data
main.py - Feature engineering → Train/test split: engineers then splits
src/data_preprocessing.py - Train/test split → scikit-learn split utility: calls
src/data_preprocessing.py - Train/test split → Feature scaling: passes partitions
src/data_preprocessing.py - Feature scaling → scikit-learn RobustScaler: fits and transforms
src/data_preprocessing.py - Workflow entry point → Feature scaling: prints output shapes
main.py