https://github.com/ssandra102/car_sales_data_science_workflow
End to End Data science workflow for Car Price prediction
https://github.com/ssandra102/car_sales_data_science_workflow
dagshub data-science mlflow mlflow-sklearn mlops-workflow
Last synced: about 1 month ago
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End to End Data science workflow for Car Price prediction
- Host: GitHub
- URL: https://github.com/ssandra102/car_sales_data_science_workflow
- Owner: ssandra102
- Created: 2023-07-31T08:31:42.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-21T18:03:23.000Z (over 2 years ago)
- Last Synced: 2025-01-02T14:31:06.638Z (over 1 year ago)
- Topics: dagshub, data-science, mlflow, mlflow-sklearn, mlops-workflow
- Language: CSS
- Homepage: https://car-sales-data-science-workflow.vercel.app
- Size: 96.1 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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
```bash
git clone https://github.com/ssandra102/car_sales_data_science_workflow.git
```
### STEP 01- Create a virtual environment after opening the repository
```bash
python -m venv mlproj
```
```bash
mlproj/Scripts/activate
```
### STEP 02- install the requirements
```bash
pip install -r requirements.txt
```
```bash
# Finally run the following command
python app.py
```
Now, open up your local host and port
## MLflow
##### cmd
- mlflow ui
### dagshub
MLFLOW_TRACKING_URI= \
MLFLOW_TRACKING_USERNAME=USERNAME \
MLFLOW_TRACKING_PASSWORD=PASSWORD \
python script.py
Run this to export as env variables:
```bash
export MLFLOW_TRACKING_URI=
export MLFLOW_TRACKING_USERNAME=
export MLFLOW_TRACKING_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.)