This repository contains a model for predicting automobile prices using Azure Machine Learning Studio. The model leverages real-time predictions and various Azure ML tools to analyze and forecast vehicle prices.
You can view the model and its real-time predictions through the following Azure ML Studio link:
Azure ML Studio Prediction Model
Below is a visual representation of the model pipeline:
- Create a Resource Group: In the Azure portal, click on the three horizontal lines, navigate to "Resource Groups," and click "Create" to set up a new resource group.
- Open Azure ML Studio: Access the workspace within your resource group.
- Design Your Pipeline: Use the Designer in Azure ML Studio to create your pipeline. Drag and drop components to build your workflow.
- Use Sample Data: Create pipelines using sample data or manually download relevant datasets (e.g., weather, automobile, health) from the web.
- Filter and Deploy: Configure the pipeline to apply filters and deploy it. Check visualizations to ensure the pipeline is working as expected.
I utilized the Data Wrangler tool within Microsoft Fabric to perform statistical summaries and visual analysis of the data.
I am using an Azure free subscription for this project, which limits certain functionalities. Due to these constraints, I cannot directly deploy the model here. However, the experience with Data Wrangler, regression, MLflow, hyperparameters, and model retraining has been highly educational.
I encourage you to explore these tools and techniques as hands-on experience is invaluable for gaining true knowledge.