How anundathescientist/real-estate-ameshousing-analysis works
1. Project shape
- This repository is a Python-based, notebook-driven data science pipeline for the Ames Housing dataset.
- The core architecture is sequential: raw data → cleaning → feature engineering → model training/comparison → dashboard.
2. Data layer
- 00 Data/Raw data/AmesHousing.csv is the original source dataset.
- Derived datasets are persisted as intermediate artifacts:
- 00 Data/Processed Data/AmesHousing_cleaned.csv
- 00 Data/Feature Matrix Data/X_features.csv
- 00 Data/Feature Matrix Data/y_target.csv
- 00 Data/Feature Matrix Data/ridge_predictions.csv
- 00 Data/Feature Matrix Data/scaler.pkl
- These files act as handoff boundaries between analysis stages instead of recomputing everything at runtime.
3. Exploratory analysis stage
- 01 EDA/01_eda_ameshousing.ipynb is the first analytical boundary.
- Its role is to inspect distributions, missingness, outliers, and relationships before transformation.
- The paired Report - EDA folder suggests this stage produces a documented findings artifact, not just code.
4. Data cleaning stage
- 02 Data Cleaning/02_data_cleaning.ipynb transforms raw housing records into a cleaned dataset.
- The output is materialized in 00 Data/Processed Data/AmesHousing_cleaned.csv.
- This stage likely owns missing-value handling, inconsistent category fixes, and basic filtering.
5. Feature engineering stage
- 03 Feature Engineering/03_feature_engineering.ipynb converts cleaned data into model-ready inputs.
- The outputs X_features.csv and y_target.csv indicate a clear split between feature matrix and target variable.
- scaler.pkl shows that feature normalization/standardization is part of the pipeline and is persisted for reuse.
6. Modeling stage
- 04 Regression/04_regression_modeling.ipynb trains several regression models on the engineered features.
- Trained model artifacts are stored under 04 Regression/Models/:
- model_linear.pkl
- model_ridge.pkl
- model_lasso.pkl
- model_gb.pkl
- This indicates a controlled comparison across linear and regularized/ensemble approaches.
7. Model selection and outputs
- 04 Regression/Best Model/ holds the selected champion model and its outputs:
- best_model.pkl
- predictions.csv
- ridge_predictions.csv in the feature data folder suggests ridge regression was also used for evaluation or downstream analysis.
- The architecture separates candidate models from the final chosen model, which is important for diagramming decision flow.
8. Model comparison stage
- 05 Model Comparison/05_model_comparison.ipynb is the evaluation boundary where model metrics are compared.
- This stage likely drives the selection of best_model.pkl and the final predictions artifact.
- Report/ here indicates the comparison results are also packaged as a narrative deliverable.
9. Dashboard layer
- 06 Dashboard/app.py is the only application runtime entry point in the repo.
- It likely consumes the saved model/artifacts rather than retraining, which makes the dashboard a read-only inference layer over the modeling outputs.
- For an architecture diagram, treat this as the presentation boundary connected to persisted model/data artifacts.
10. Operational and environment boundaries
- requirements.txt defines the Python dependency set for notebooks, training, and dashboard execution.
- .devcontainer/devcontainer.json shows the project is meant to run in a reproducible containerized dev environment.
- LICENSE and the various Report folders indicate this repo is also packaged as a deliverable, not only a codebase.
11. Overall data flow
- The main flow is:
- Raw CSV ingestion
- Notebook-based cleaning
- Feature generation and scaling
- Regression training
- Model comparison
- Saved artifacts consumed by the dashboard
- The key architectural characteristic is artifact persistence between stages, with CSVs and pickle files serving as the integration points.
12. What to draw as boundaries
- Draw separate subsystems for:
- Data storage
- EDA/cleaning notebooks
- Feature engineering
- Model training and selection
- Dashboard/app runtime
- The strongest dependency arrows should run from raw data into notebooks, and from saved model/artifact files into the dashboard.
Main entry points: 03_feature_engineering.ipynb Feature Notebook · 05_model_comparison.ipynb Compare Notebook · 04_regression_modeling.ipynb Regression Notebook · requirements.txt Requirements
Components 21
Data Layer
Raw, processed, and feature artifacts passed between notebook stages.
- Raw CSV · Original Ames Housing source data.
00 Data/Raw data/AmesHousing.csv - Cleaned Data · Processed housing records after cleaning and normalization.
00 Data/Processed Data/AmesHousing_cleaned.csv - Features · Model-ready input matrix used for training and inference preparation.
00 Data/Feature Matrix Data/X_features.csv - Target · Regression target values separated from the feature matrix.
00 Data/Feature Matrix Data/y_target.csv - Scaler · Persisted normalization/standardization transform used across modeling steps.
00 Data/Feature Matrix Data/scaler.pkl
Preparation
Exploration, cleaning, and feature engineering notebooks.
- EDA Notebook · Explores distributions, missingness, outliers, and relationships.
01 EDA/01_eda_ameshousing.ipynb - Cleaning Notebook · Transforms raw records into the cleaned dataset.
02 Data Cleaning/02_data_cleaning.ipynb - Feature Notebook · Builds model-ready features and target splits.
03 Feature Engineering/03_feature_engineering.ipynb
Modeling
Regression training, comparison, and saved model artifacts.
- Regression Notebook · Trains and evaluates candidate regression models.
04 Regression/04_regression_modeling.ipynb - Candidate Models · Trained linear, ridge, lasso, and gradient boosting model files.
04 Regression/Models - Best Model · Champion model artifact chosen after comparison.
04 Regression/Best Model/best_model.pkl - Predictions · Final prediction output generated from the selected model.
04 Regression/Best Model/predictions.csv - Ridge Predictions · Auxiliary ridge prediction artifact used in analysis or comparison.
00 Data/Feature Matrix Data/ridge_predictions.csv - Compare Notebook · Compares metrics and drives final model selection.
05 Model Comparison/05_model_comparison.ipynb
Presentation
Dashboard runtime and narrative report outputs.
- EDA Report · Narrative findings artifact produced from the exploratory analysis stage.
01 EDA/Report - EDA - Cleaning Report · Documented cleaning-stage outputs and notes.
02 Data Cleaning/Report - Feature Report · Narrative outputs for the feature engineering stage.
03 Feature Engineering/Report - Compare Report · Packaged comparison findings and decision rationale.
05 Model Comparison/Report - Dashboard App · Read-only application layer that consumes saved artifacts for presentation.
06 Dashboard/app.py
Ops
Environment and dependency definitions for reproducible execution.
- Requirements · Python dependency set for notebooks, training, and dashboard execution.
requirements.txt - Dev Container · Reproducible development environment definition.
.devcontainer/devcontainer.json
Connections 25
- Raw CSV → EDA Notebook: inspect · EDA starts from the raw housing dataset.
- Raw CSV → Cleaning Notebook: clean · Cleaning notebook ingests the original dataset.
- EDA Notebook → EDA Report: document · Exploratory findings are packaged into a report.
- Cleaning Notebook → Cleaned Data: write · Cleaning stage persists the cleaned dataset.
- Cleaning Notebook → Cleaning Report: document · Cleaning results are summarized in a report.
- Cleaned Data → Feature Notebook: input · Feature engineering consumes cleaned data.
- Feature Notebook → Features: write · Feature matrix is materialized as a CSV artifact.
- Feature Notebook → Target: write · Target values are stored separately from features.
- Feature Notebook → Scaler: persist · Scaling transform is saved for reuse.
- Feature Notebook → Feature Report: document · Feature engineering outputs are summarized.
- Features → Regression Notebook: train on · Regression training uses engineered features.
- Target → Regression Notebook: target · Regression training uses the separated target values.
- Scaler → Regression Notebook: transform · Models rely on the persisted scaling pipeline.
- Regression Notebook → Candidate Models: fit · Notebook produces the set of trained candidate models.
- Candidate Models → Compare Notebook: evaluate · Comparison stage scores the candidate model artifacts.
- Ridge Predictions → Compare Notebook: compare · Auxiliary ridge predictions support evaluation.
- Compare Notebook → Best Model: select · Comparison selects the champion model artifact.
- Compare Notebook → Predictions: export · Final outputs are written from the selected model.
- Compare Notebook → Compare Report: document · Model comparison results are packaged as a report.
- Best Model → Dashboard App: serve · Dashboard consumes the selected model for inference.
- Predictions → Dashboard App: display · Dashboard can present saved prediction outputs.
- Requirements → EDA Notebook: supports · Dependency specification supports notebook execution.
- Requirements → Regression Notebook: supports · Dependency specification supports training and comparison.
- Requirements → Dashboard App: supports · Dependency specification supports application runtime.
- Dev Container → Requirements: pins · Containerized dev environment complements dependency setup.