How tulsip26-hub/cementstrength_predictionmodel works
This repository is a small end-to-end concrete-strength regression pipeline. Running main.py starts ingestion, passes the resulting dataframe to preprocessing for duplicate removal, feature/target separation, train-test splitting, and scaling, then passes the prepared data to model building. The model-building stage trains a Random Forest, predicts on the test set, and returns an R² score for printing. The README also identifies data/Concrete_Data.csv as the dataset; however, the sampled loader fetches CSV data from a remote GitHub raw-file URL, so the local file’s runtime use is not established.
Main entry points: main.py Pipeline entry · data_ingestion.py Data ingestion · model_building.py Random Forest and R² · data_preprocessing.py Preprocessing
Components 6
Pipeline orchestration
- Pipeline entry
main.py
Data preparation
- Data ingestion
src/data_ingestion.py - Preprocessing
src/data_preprocessing.py
Modeling and evaluation
- Random Forest and R²
src/model_building.py
Other components
- Pipeline runner
- Remote CSV source
Connections 7
- Pipeline runner → Pipeline entry: runs
- Pipeline entry → Data ingestion: calls
- Data ingestion → Remote CSV source: fetches CSV
- Data ingestion → Pipeline entry: returns dataframe
- Pipeline entry → Preprocessing: passes dataframe
- Pipeline entry → Random Forest and R²: passes splits
- Random Forest and R² → Pipeline entry: returns R²