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RuralCredit is a financial service provided to individuals and businesses in rural areas. It provides loan that help support agricultural activities, rural development, and livelihoods in rural communities.
RuralCredit plays a crucial role in empowering farmers, small businesses, and rural entrepreneurs by providing them with the necessary funds to invest in their operations, purchase equipment, and improve their productivity. It helps bridge the gap between the financial needs of rural communities and the availability of financial resources.
By facilitating access to credit, rural credit contributes to the overall economic growth and development of rural areas, reduces poverty, and enhances the standard of living for rural populations.
bash setup.sh
source activate ./venv
config/config.yaml
raw_schema.yaml/processed_schema.yaml
(if needed)params.yaml
(if needed)src/RuralCreditPredictor/entity/config_entity.py
src/RuralCreditPredictor/config/configuration.py
src/RuralCreditPredictor/components
src/RuralCreditPredictor/pipeline
main.py
app.py
Commands to set up the MLFlow environment:
export MLFLOW_TRACKING_URI=https://dagshub.com/heydido/RuralCreditPredictor.mlflow
export MLFLOW_TRACKING_USERNAME=heydido
export MLFLOW_TRACKING_PASSWORD=********
Get these from the DAGsHub server.
Commands to set up the DVC environment:
Initialize DVC:
dvc init
Write the DVC file: dvc.yaml
Run the DVC pipeline:
dvc repro
Note:
"dvc repro" will fail if you haven't exported the MLFlow variables as explained in the MLFlow Setup section.
Track each experiment changes with git:
git add .
git commit -m "DVC: <message>"
git push origin main
Add DVC Remote (one-time):
dvc remote add origin s3://dvc
dvc remote modify origin endpointurl https://dagshub.com/heydido/RuralCreditPredictor.s3
Push the DVC changes:
dvc push
for better clarity, visit the DAGsHub repo: RuralCreditPredictor
This project is created using a public template I created: MLProjectTemplate
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