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car_sales_data_science_workflow

Workflows

  1. Update config.yaml
  2. Update schema.yaml
  3. Update params.yaml
  4. Update the entity
  5. Update the configuration manager in src config
  6. Update the components
  7. Update the pipeline
  8. Update the main.py
  9. Update the app.py

Stages

  1. Data Ingestion
  2. Data validation
  3. Data Transformation
  4. Model Training
  5. Model Evaluation

Tech Stack

  1. Python
  2. Sci-kit Learn
  3. MLFlow
  4. Dagshub

How to run?

STEPS:

Clone the repository

git clone https://github.com/ssandra102/car_sales_data_science_workflow.git

STEP 01- Create a virtual environment after opening the repository

python -m venv mlproj
mlproj/Scripts/activate

STEP 02- install the requirements

pip install -r requirements.txt
# Finally run the following command
python app.py

Now, open up your local host and port

MLflow

cmd
  • mlflow ui

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/{USERNAME}/{REPO_NAME}.mlflow
MLFLOW_TRACKING_USERNAME=USERNAME
MLFLOW_TRACKING_PASSWORD=PASSWORD
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=<https://dagshub.com/{USERNAME}/{REPO NAME}.mlflow>

export MLFLOW_TRACKING_USERNAME=<USERNAME>

export MLFLOW_TRACKING_PASSWORD=<PASSWORD>

Flask Web App

hosted in Microsoft Azure : https://car-price-prediction-webapp.azurewebsites.net

       

       

(note: the values entered in the form are random. The predicted car price is in Lakhs.)