This is the Python SDK for PredictEasy, a platform for predictive analytics. With this SDK, you can easily interact with PredictEasy's APIs to perform various tasks such as managing datasources, performing regression analysis, classification, and clustering.

To install the PredictEasy Python SDK, you can use pip:

pip install predicteasy

Here's a detailed tutorial on how to use each public method in the PredictEasyClient class:

1. list_datasources()

   This method retrieves a list of all datasources available in PredictEasy.

   from predicteasy import PredictEasyClient
   
   # Initialize the client
   client = PredictEasyClient(auth_key="your_auth_key", auth_secret="your_auth_secret")
   
   # List all datasources
   datasources = client.datasource.list_datasources()
   print(datasources)

2. getDatasource(datasource_id)

   This method fetches a specific datasource by its ID.

   # Fetch a specific datasource by ID
   datasource_id = "your_datasource_id"
   datasource = client.datasource.getDatasource(datasource_id)
   print(datasource)

3. createDatasource(title, description, horizontal, vertical, file_path)

   This method creates a new datasource.

   # Define datasource parameters
   title = "Sample Title"
   description = "Sample Description"
   horizontal = ['CRM']
   vertical = "Telecom"
   file_path = "path/to/your/dataset.csv"
   
   # Create a new datasource
   new_datasource = client.datasource.createDatasource(title, description, horizontal, vertical, file_path)
   print(new_datasource)

4. deleteDatasource(datasource_id)

   This method deletes a datasource by its ID.

   # Delete a datasource by ID
   datasource_id = "datasource_to_delete_id"
   response = client.datasource.deleteDatasource(datasource_id)
   print(response)

5. regression.regression(datasource_id, title, test_size, cross_val, x, y)

   This method performs regression analysis.

   # Perform regression analysis
   regression_result = client.regression.regression("datasource_id", "Sales", 0.2, 2, ["feature1", "feature2"], "target")
   regression_result

6. classification.classify(datasource_id, title, test_size, cross_val, x, y)

   This method performs classification.

   # Perform classification
   classification_result = client.classification.classify("datasource_id", "Ad Click", 0.2, 2, ["feature1", "feature2"], "target")
   classification_result

7. clustering.cluster(datasource_id, title, exclude, n_clusters)

   This method performs clustering.

   # Perform clustering
   clustering_result = client.clustering.cluster("datasource_id", "Title", ["feature_to_exclude"], 3)
   clustering_result

Replace "your_auth_key" and "your_auth_secret" with your actual credentials from your PredictEasy Developer Profile and "your_datasource_id" with your Datasource IDs.