https://github.com/vikpires/ds_bike-rentals-automl
Exploring automated machine learning in Azure Machine Learning using the Bike Rentals dataset.
https://github.com/vikpires/ds_bike-rentals-automl
ai900 azure azure-machine-learning data-science dio-bootcamp machine-learning
Last synced: about 1 month ago
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Exploring automated machine learning in Azure Machine Learning using the Bike Rentals dataset.
- Host: GitHub
- URL: https://github.com/vikpires/ds_bike-rentals-automl
- Owner: vikpires
- License: mit
- Created: 2025-01-22T08:55:57.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-24T06:20:02.000Z (about 2 months ago)
- Last Synced: 2025-03-24T07:29:10.630Z (about 2 months ago)
- Topics: ai900, azure, azure-machine-learning, data-science, dio-bootcamp, machine-learning
- Homepage:
- Size: 238 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Azure Machine Learning
> Challenge created as part of the Microsoft AI Fundamentals Bootcamp by DIO, based on the [Explore Automated Machine Learning in Azure Machine Learning](https://aka.ms/ai900-auto-ml) tutorial.
## Automated ML: Step-by-Step Guide
This guide outlines the process of using Automated Machine Learning (AutoML) in Azure Machine Learning to train and deploy a regression model using the `bike-rentals` dataset.
### 1. Provision an Azure Machine Learning Workspace
- Start by provisioning an Azure Machine Learning workspace in the Azure portal.
- Search for *Machine Learning* in the Azure portal and create a new Azure Machine Learning resource.
- Navigate to the Azure Machine Learning Studio to manage your machine learning resources.
- In Azure Machine Learning Studio, select the *Automated ML* option.
- Set a descriptive name and description for your job.
- Choose the task type. For this project, select **Regression**.
### 2. Choose the Dataset
- Use the `bike-rentals` dataset, which is available [here](https://aka.ms/bike-rentals).
### 3. Configure Task Settings
- Adjust the task settings, including:
- Metric definitions
- Allowed models
- Limits and settings for validation and testing
```
Task settings:
Task type: Regression
Dataset: bike-rentals
Target column: rentals (integer)
Additional configuration settings:
Primary metric: NormalizedRootMeanSquaredError
Explain best model: Unselected
Enable ensemble stacking: Unselected
Use all supported models: Unselected. You’ll restrict the job to try only a few specific algorithms.
Allowed models: Select only RandomForest and LightGBM — normally you’d want to try as many as possible, but each model added increases the time it takes to run the job.
Limits: Expand this section
Max trials: 3
Max concurrent trials: 3
Max nodes: 3
Metric score threshold: 0.085 (so that if a model achieves a normalized root mean squared error metric score of 0.085 or less, the job ends.)
Experiment timeout: 15
Iteration timeout: 15
Enable early termination: Selected
Validation and test:
Validation type: Train-validation split
Percentage of validation data: 10
Test dataset: None
```
### 4. Set Compute Characteristics
- Configure the compute settings for model training as shown below:
```
Compute:
Select compute type: Serverless
Virtual machine type: CPU
Virtual machine tier: Dedicated
Virtual machine size: Standard_DS3_V2*
Number of instances: 1
```
### 5. Submit the Training Job
- After configuring the settings, submit the training job to begin the model training process.
### 6. Trained Models
- Once the AutoML job is complete, review the best model trained by the system.
- In this training, three models were generated:
- VotingEnsemble
- LightGBMRegressor
- Random Forest
### 7. Identify the Best Model
- The best model was the `VotingEnsemble`, which had the best training performance.
### 8. Review the Data Transformation
- The following diagram illustrates the data preprocessing, feature engineering, scaling techniques and the machine learning algorithm that Automated ML applied to generate this particular model.
### 9. Review Model Metrics
- In the *Metrics* tab, you can view all metrics related to the trained model and the data processing:
### 10. Analyze Residuals
- The *Residuals* graph shows the distribution of errors made by the model:
### 11. Compare Predicted vs. True Values
- The graph below compares the predicted values with the true values from the test set:
Comparation of predicted and true values graphs
### 12. Deploy the Best Model
- With the best model selected, you can deploy it. In this tutorial the `Real-time endpoint` option was used to deploy the model.
### 13. Test the Deployed Model
- Once the deployment is complete, you can test the model by sending requests to the `Real-time endpoint`.
### 14. Input Data for Testing
- To test the endpoint, replace the sample JSON input with your own data. Below is an example of the input used:
Input:
```
{
"input_data": {
"data": [
{
"day": 1,
"mnth": 1,
"year": 2022,
"season": 2,
"holiday": 0,
"weekday": 1,
"workingday": 1,
"weathersit": 2,
"temp": 0.3,
"atemp": 0.3,
"hum": 0.3,
"windspeed": 0.3
}
]
},
"GlobalParameters": 1.0
}
```
### 15. Output
```
[
353.571393707175
]
```
### 16. Model Summary
- In summary, this model predicts the number of bicycle rentals on a given day based on features such as season, weather, temperature, humidity, and time of day.