How vaibhav8831/ai-impact-jobs-layoff-prediction_model works
This repository is a batch machine-learning project for predicting job layoff risk. Running main.py loads the bundled CSV, prepares and splits the data, trains Random Forest and XGBoost pipelines, and evaluates both prediction sets with weighted classification metrics. The graph reflects the sampled Python workflow; the README provides no further product details, and app.py is empty.
Main entry points: main.py Workflow Entry · data_ingation.py CSV Loader · data_modelbuilding.py Model Training · ai-impact-jobs-layoff-risk-dataset.csv Risk Dataset
Components 7
Data Input
- Risk Dataset
data/ai-impact-jobs-layoff-risk-dataset.csv - CSV Loader
src/data_ingation.py
Modeling
- Workflow Entry
main.py - Data Preparation
src/data_preproces.py - Model Training
src/data_modelbuilding.py
Evaluation
- Metric Evaluation
src/modelevolation.py
Other components
- Run Script
Connections 8
- Run Script → Workflow Entry: runs
- Workflow Entry → CSV Loader: calls
- CSV Loader → Risk Dataset: reads
- CSV Loader → Workflow Entry: returns data
- Workflow Entry → Model Training: calls
- Model Training → Data Preparation: calls
- Model Training → Workflow Entry: returns predictions
- Workflow Entry → Metric Evaluation: evaluates predictions