Housing Price Prediction Using Machine Learning is to predict the data of housings. Here I have included many data features. The most important feature according to the builders perspective view would be Bedroom. Sometimes it’s very important for a builder to check which is the highest selling house type which enables the builder to make houses which are more than three bedrooms. In the output section you would be seeing the visualization of three bedroom houses that are most commonly sold followed by four bedrooms. The main aim of this project is to make a model which can give a good prediction on the price of the house based on other variables.
STEP 6 : In Basic Section under project details select your subscription and resource group. After that in Workspace details specify your Workspace name and select your nearest region.
STEP 8 : After a sucessfull deployment launched Azure Machine Learning Studio to build and deploy the machine learning models.
The following prerequisites are required to make this repository work:
- Azure subscription
- Contributor access to the Azure subscription
- Access to GitHub Actions
Create a new repositary in GitHub and save the file.
Go to Resource group and use azure cloud shell.
# Replace {service-principal-name}, {subscription-id} and {resource-group} with your
# Azure subscription id and resource group name and any name for your service principle
az ad sp create-for-rbac --name {"service-principal-name"} --role contributor --scopes /subscriptions/{subscription-id}/resourceGroups/{resource-group} --sdk-authThis will generate the following JSON output:
{
"clientId": "<GUID>",
"clientSecret": "<GUID>",
"subscriptionId": "<GUID>",
"tenantId": "<GUID>",
(...)
}Add this JSON output as a secret with the name AZURE_CREDENTIALS in GitHub repository
You have to modify the parameters in the "/.cloud/.azure/workspace.json">/.cloud/.azure/workspace.json".
Please use the same value for the resource_group parameter that you have used when generating the azure credentials. If you already have an Azure ML Workspace under that resource group, change the name parameter in the JSON file to the name of your workspace, if you want the Action to create a new workspace in that resource group, pick a name for your new workspace, and assign it to the name parameter. You can also delete the name parameter, if you want the action to use the default value, which is the repository name. Once you save your changes to the file, the predefined GitHub workflow that trains and deploys a model on Azure Machine Learning gets triggered. Check the actions tab to view if your actions have successfully run.
Now you can start modifying the code in the code folder, so that your model and not the provided sample model gets trained on Azure. Where required, modify the environment yaml so that the training and deployment environments will have the correct packages installed in the conda environment for your training and deployment. Upon pushing the changes, actions will kick off your training and deployment run. Check the actions tab to view if your actions have successfully run.
The log outputs of your action will provide URLs for you to view the resources that have been created in AML. Alternatively, you can visit the Machine Learning Studio to view the progress of your runs, etc.
SHREYA SHREE PADHI
FACULTY OF ENGINEERING AND TECHNOLOGY, JAIN UNIVERSITY
19BTRCY030 (BTECH 3RD YEAR)