# ashly-06/heavy-equipment-selling-price-prediction architecture

> Architecture diagram of the GitHub repository ashly-06/heavy-equipment-selling-price-prediction, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-12). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/ashly-06/heavy-equipment-selling-price-prediction
- Repository: https://github.com/ashly-06/heavy-equipment-selling-price-prediction

## Overview

This repository is a notebook-based regression project for predicting heavy-equipment sale prices. Its documented workflow starts with the Kaggle training data, explores and prepares it, engineers features, trains and evaluates three tree-based models, combines predictions, and generates a submission. The README describes these stages, but the notebook contents were not excerpted, so the graph reflects documented relationships rather than verified calls or implementation details.

Main entry points: [`heavy-equipment-selling-price-prediction_py.ipynb`](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb) (Feature engineering)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data preparation"]
  node_eda["Exploratory analysis"]
  node_preprocessing["Preprocessing"]
  node_features["Feature engineering"]
end

subgraph group_models["Modeling and evaluation"]
  node_catboost["CatBoost model"]
  node_lightgbm["LightGBM model"]
  node_xgboost["XGBoost model"]
  node_tuning["CatBoost tuning"]
  node_evaluation["Model evaluation"]
end

subgraph group_results["Prediction and results"]
  node_ensemble["Weighted ensemble"]
  node_submission["Submission generation"]
end

node_analyst(("Analyst"))
node_kaggle_data[("Kaggle data")]
node_leaderboard(("Kaggle leaderboard"))

node_analyst -->|"explores data"| node_eda
node_kaggle_data -->|"provides records"| node_eda
node_eda -->|"informs preparation"| node_preprocessing
node_preprocessing -->|"prepares inputs"| node_features
node_features -->|"trains model"| node_catboost
node_features -->|"trains model"| node_lightgbm
node_features -->|"trains model"| node_xgboost
node_catboost -->|"tunes hyperparameters"| node_tuning
node_catboost -->|"evaluates with RMSLE"| node_evaluation
node_lightgbm -->|"evaluates with RMSLE"| node_evaluation
node_xgboost -->|"evaluates with RMSLE"| node_evaluation
node_catboost -->|"contributes predictions"| node_ensemble
node_lightgbm -->|"contributes predictions"| node_ensemble
node_xgboost -->|"contributes predictions"| node_ensemble
node_ensemble -->|"generates submission"| node_submission
node_submission -->|"evaluated on"| node_leaderboard

click node_eda "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_preprocessing "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_features "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_catboost "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_lightgbm "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_xgboost "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_tuning "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_evaluation "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_ensemble "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb"
click node_submission "https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.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_eda,node_preprocessing,node_features toneBlue
class node_catboost,node_lightgbm,node_xgboost,node_tuning,node_evaluation,node_kaggle_data toneAmber
class node_ensemble,node_submission toneMint
class node_analyst,node_leaderboard toneIndigo
```

## Components

### Data preparation

- [Exploratory analysis](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [Preprocessing](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [Feature engineering](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`

### Modeling and evaluation

- [CatBoost model](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [LightGBM model](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [XGBoost model](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [CatBoost tuning](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [Model evaluation](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`

### Prediction and results

- [Weighted ensemble](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`
- [Submission generation](https://github.com/ashly-06/heavy-equipment-selling-price-prediction/blob/main/heavy-equipment-selling-price-prediction_py.ipynb): `heavy-equipment-selling-price-prediction_py.ipynb`

### Other components

- **Analyst**
- **Kaggle data**
- **Kaggle leaderboard**

## Connections

- Analyst → Exploratory analysis: explores data (evidence: `README.md`)
- Kaggle data → Exploratory analysis: provides records (evidence: `README.md`)
- Exploratory analysis → Preprocessing: informs preparation (evidence: `README.md`)
- Preprocessing → Feature engineering: prepares inputs (evidence: `README.md`)
- Feature engineering → CatBoost model: trains model (evidence: `README.md`)
- Feature engineering → LightGBM model: trains model (evidence: `README.md`)
- Feature engineering → XGBoost model: trains model (evidence: `README.md`)
- CatBoost model → CatBoost tuning: tunes hyperparameters (evidence: `README.md`)
- CatBoost model → Model evaluation: evaluates with RMSLE (evidence: `README.md`)
- LightGBM model → Model evaluation: evaluates with RMSLE (evidence: `README.md`)
- XGBoost model → Model evaluation: evaluates with RMSLE (evidence: `README.md`)
- CatBoost model → Weighted ensemble: contributes predictions (evidence: `README.md`)
- LightGBM model → Weighted ensemble: contributes predictions (evidence: `README.md`)
- XGBoost model → Weighted ensemble: contributes predictions (evidence: `README.md`)
- Weighted ensemble → Submission generation: generates submission (evidence: `README.md`)
- Submission generation → Kaggle leaderboard: evaluated on (evidence: `README.md`)