How sifat192/house-price-prediction works
This repository is a notebook-based Ames housing price prediction project. Its documented workflow explores the data, preprocesses and engineers features, trains a Linear Regression model, and evaluates predictions. The notebook is the only implementation file in the tree, so it represents each documented stage; the notebook itself was not sampled, and these links reflect README documentation rather than inspected calls.
Main entry points: House_price_prediction_model.ipynb Exploration · train.csv Ames data
Components 8
Data analysis
- Ames data
train.csv - Exploration
House_price_prediction_model.ipynb - Preprocessing
House_price_prediction_model.ipynb - Feature engineering
House_price_prediction_model.ipynb
Modeling
- Linear Regression
House_price_prediction_model.ipynb - Model evaluation
House_price_prediction_model.ipynb - Price predictions
House_price_prediction_model.ipynb
Other components
- Analyst
Connections 7
- Analyst → Exploration: runs notebook
README.md - Ames data → Exploration: provides data
README.md - Exploration → Preprocessing: informs
README.md - Preprocessing → Feature engineering: precedes
README.md - Feature engineering → Linear Regression: feeds features
README.md - Linear Regression → Model evaluation: evaluated by
README.md - Model evaluation → Price predictions: compares predictions
README.md