OBJECTIVE:

Sale Price Prediction using Azure Machine Learning Studio with Excel API integration that helps you predict house prices based on a machine learning algorithm (K-means Clustering).

1. Create a Machine Learning Model with Azure Machine Learning Studio

• Explore and visualize datasets with Python in Jupyter Notebooks.
• Use Z-score Transformation method to normalize data.
• Train a regression model (supervised learning).
• Evaluate model.

Figure: Lemonade Training

2. Publish trained model to a webservice and use it to predict labels from new feature data.

• Deploy the web service.
• Consume the web service with Excel.

Figure: Predict lemonade sales

3. Train a classification model (supervised learning) in Azure Machine Learning Studio

Figure: Lemonade classification

4. Train a clustering model (unsupervised learning)

• Copy an existing training experiment from the Azure AI Gallery and run it to train a K-Means clustering model that segments specific customers into clusters based on similarities in their features.

Figure: Lemonade clustering customers

Published Links to my Azure Machine Learning Environment

• Lemonade Training
• Predict Lemonade Sales
• Lemonade Clustering Customers