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This project helps to predict the compressive strength of concrete after a given time at any time which in general takes 28 days time by the industry. The proposed Machine Learning Project is a time saver.
The Concrete compressive strength model is a Machine Learning project which predicts Concrete compressive strength on the basis of raw materials and age of the concrete.
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To run this project, you will need to add the following environment variables to your .env file
API_KEY
ANOTHER_API_KEY
bash init_setup.sh
conda activate ./env
python src/Concrete_CS/pipeline/stage_01_data_ingestion.py
python src/Concrete_CS/pipeline/stage_02_data_validation.py
python src/Concrete_CS/pipeline/stage_03_data_transformation.py
python src/Concrete_CS/pipeline/stage_04_model_trainer.py
dvc init
dvc repro
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commented in commit6fdace532eon branch master
1 year ago OutdatedConcrete Compressive Strength
This project helps to predict the compressive strength of concrete after a given time at any time which in general takes 28 days time by the industry. The proposed Machine Learning Project is a time saver.
Acknowledgements
Appendix
The Concrete compressive strength model is a Machine Learning project which predicts Concrete compressive strength on the basis of raw materials and age of the concrete.
Authors
Badges
Add badges from somewhere like: shields.io
Environment Variables
To run this project, you will need to add the following environment variables to your .env file
API_KEY
ANOTHER_API_KEY
Configuration Setup
Command to the whole setup from scratch
For Virtual Environment and Requirements installation
Activate the Environment
Data Ingestion Step
Data Validation Step
Data Transformation Step
Model Trainer Step
DVC
Used to know the Model Flow
To initialize DVC
To run the Pipeline