{"id":19066467,"url":"https://github.com/ssandra102/car_sales_data_science_workflow","last_synced_at":"2026-05-15T17:30:16.437Z","repository":{"id":186500654,"uuid":"672818001","full_name":"ssandra102/car_sales_data_science_workflow","owner":"ssandra102","description":"End to End Data science workflow for Car Price prediction ","archived":false,"fork":false,"pushed_at":"2023-12-21T18:03:23.000Z","size":100717,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-02T14:31:06.638Z","etag":null,"topics":["dagshub","data-science","mlflow","mlflow-sklearn","mlops-workflow"],"latest_commit_sha":null,"homepage":"https://car-sales-data-science-workflow.vercel.app","language":"CSS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ssandra102.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-07-31T08:31:42.000Z","updated_at":"2024-05-08T01:42:35.000Z","dependencies_parsed_at":"2025-01-02T17:17:39.553Z","dependency_job_id":null,"html_url":"https://github.com/ssandra102/car_sales_data_science_workflow","commit_stats":null,"previous_names":["ssandra102/car_sales_data_science_workflow"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssandra102%2Fcar_sales_data_science_workflow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssandra102%2Fcar_sales_data_science_workflow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssandra102%2Fcar_sales_data_science_workflow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssandra102%2Fcar_sales_data_science_workflow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ssandra102","download_url":"https://codeload.github.com/ssandra102/car_sales_data_science_workflow/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240118417,"owners_count":19750491,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dagshub","data-science","mlflow","mlflow-sklearn","mlops-workflow"],"created_at":"2024-11-09T00:56:53.630Z","updated_at":"2026-05-15T17:30:16.386Z","avatar_url":"https://github.com/ssandra102.png","language":"CSS","funding_links":[],"categories":[],"sub_categories":[],"readme":"# car_sales_data_science_workflow\n\n\n## Workflows\n\n1. Update config.yaml\n2. Update schema.yaml\n3. Update params.yaml\n4. Update the entity\n5. Update the configuration manager in src config\n6. Update the components\n7. Update the pipeline \n8. Update the main.py\n9. Update the app.py\n\n## Stages\n\n1. Data Ingestion\n2. Data validation\n3. Data Transformation\n4. Model Training\n5. Model Evaluation\n\n## Tech Stack\n1. Python\n2. Sci-kit Learn\n3. MLFlow\n4. Dagshub\n   \n# How to run?\n### STEPS:\n\nClone the repository\n\n```bash\ngit clone https://github.com/ssandra102/car_sales_data_science_workflow.git\n```\n### STEP 01- Create a virtual environment after opening the repository\n\n```bash\npython -m venv mlproj\n```\n\n```bash\nmlproj/Scripts/activate\n```\n\n\n### STEP 02- install the requirements\n```bash\npip install -r requirements.txt\n```\n\n\n```bash\n# Finally run the following command\npython app.py\n```\n\nNow, open up your local host and port\n\n\n\n## MLflow\n##### cmd\n- mlflow ui\n\n### dagshub\nMLFLOW_TRACKING_URI=\u003chttps://dagshub.com/{USERNAME}/{REPO_NAME}.mlflow\u003e \\\nMLFLOW_TRACKING_USERNAME=USERNAME \\\nMLFLOW_TRACKING_PASSWORD=PASSWORD \\\npython script.py\n\nRun this to export as env variables:\n\n```bash\n\nexport MLFLOW_TRACKING_URI=\u003chttps://dagshub.com/{USERNAME}/{REPO NAME}.mlflow\u003e\n\nexport MLFLOW_TRACKING_USERNAME=\u003cUSERNAME\u003e\n\nexport MLFLOW_TRACKING_PASSWORD=\u003cPASSWORD\u003e\n\n```\n\n\n## Flask Web App\nhosted in Microsoft Azure : https://car-price-prediction-webapp.azurewebsites.net\n\u003cp align=\"left\"\u003e\n\u003cimg src =\"https://github.com/ssandra102/car_sales_data_science_workflow/assets/72643907/51fb23aa-731f-48bf-9655-d3923bf1a82c\"\u003e\n\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\n  \n\n\u003cp align=\"left\"\u003e\n\u003cimg src =\"https://github.com/ssandra102/car_sales_data_science_workflow/assets/72643907/84c9b46c-20dc-4893-b552-980bc8b54e08\"\u003e\n\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\n\n(note: the values entered in the form are random. The predicted car price is in Lakhs.)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fssandra102%2Fcar_sales_data_science_workflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fssandra102%2Fcar_sales_data_science_workflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fssandra102%2Fcar_sales_data_science_workflow/lists"}