Learn more and consistency with other samples

LEARN_MORE.md

@@ -0,0 +1,70 @@
+# Churn Predictor: Learn More
+
+This document provides more detail about the sample churn predictor.
+You can learn more about building churn predictors with GraphLab Create in
+the [user guide](https://dato.com/learn/userguide/churn_prediction/quick-start.html).
+
+## How it works
+
+The `churn_predictor.py` script performs the following actions:
+
+```
+Load Data -> Prepare Data -> Train Model -> Explore Model
+```
+
+### Load Data
+
+This sample uses a table of product purchase data (541909 product purchases):
+
+| InvoiceNo | StockCode |          Description           | Quantity | InvoiceDate  | UnitPrice | CustomerID |    Country     |
+| --------- | --------- | ------------------------------ | -------- | ------------ | --------- | ---------- | -------------- |
+|   536365  |   85123A  | WHITE HANGING HEART T-LIGH...  |    6     | 12/1/10 8:26 |    2.55   |   17850    | United Kingdom |
+|   536365  |   71053   |      WHITE METAL LANTERN       |    6     | 12/1/10 8:26 |    3.39   |   17850    | United Kingdom |
+|   536365  |   84406B  | CREAM CUPID HEARTS COAT HANGER |    8     | 12/1/10 8:26 |    2.75   |   17850    | United Kingdom |
+|   536365  |   84029G  | KNITTED UNION FLAG HOT WAT...  |    6     | 12/1/10 8:26 |    3.39   |   17850    | United Kingdom |
+|   536365  |   84029E  | RED WOOLLY HOTTIE WHITE HEART. |    6     | 12/1/10 8:26 |    3.39   |   17850    | United Kingdom |
+
+
+### Prepare Data
+
+Once the data is loaded, you typically want to clean it up or transform it in various ways.
+The Churn Prediction toolkit uses GraphLab Create's TimeSeries data structure.
+The sample code parses the `InvoiceDate` text column and converts
+it to Python datetime objects. From this dataset, we create a TimeSeries.
+
+
+### Train Churn Predictor Model
+
+Next, we randomly split the data into a training set and validation set,
+and define the churn period and boundary. These settings define time
+after which a user will be considered "churned". To learn more,
+check out the [user guide](https://dato.com/learn/userguide/churn_prediction/quick-start.html#how-is-churn-defined).
+
+We use [`gl.churn_predictor.create`](https://dato.com/products/create/docs/generated/graphlab.churn_predictor.create.html)
+to build the prediction model on the training set.
+
+
+### Explore the Model
+
+Once you've trained the model, you can use it to make churn forecasts.
+The sample script launches an interactive web-based view for exploring
+and evaluating the model:
+
+![Screenshot of Exploration](/assets/churn-explore.png)
+
+![Screenshot of Evaluation](/assets/churn-evaluate.png)
+
+Find more information about how to use your model
+in the [user guide](https://dato.com/learn/userguide/churn_prediction/using-a-trained-model.html).
+
+
+## Use your own data
+
+You can replace the sample data provided in this project with your own
+dataset. At minimum, your data must contain a time column, a user id column, and a feature column.
+
+You will need to replace the file name and column names in the
+`churn_predictor.py` script to refer to your own data set.
+
+For more information about setting up a churn predictor model,
+check out the [user guide](https://dato.com/learn/userguide/churn_prediction/quick-start.html).

README.md

@@ -27,8 +27,9 @@ customers who are likely to stop using your product or service.
   python download_data.py
   ```
 
-4. Making sure you are working in a Python environment with GraphLab Create installed,
-   run the `churn_predictor.py` script to build and explore the model on your machine:
+4. Make sure you are working in a Python environment with GraphLab Create installed
+   (e.g. if you installed GraphLab Create using the Dato Launcher, you can use the Launcher to open a GraphLab Create Terminal).
+   Then, run the `churn_predictor.py` script to build and explore the model on your machine:
 
    ```bash
    python -i churn_predictor.py
@@ -37,17 +38,33 @@ customers who are likely to stop using your product or service.
    The `-i` flag causes Python to drop into an interactive interpreter
    after the script executes.
 
-   Once the model has been created, a browser window should open
-   to let you explore and interact with your model.
-
    Alternatively, you can also run the provided IPython Notebook:
 
    ```bash
    ipython notebook churn_predictor.ipynb
    ```
 
+   Once the model has been created, a browser window should open
+   to let you explore and interact with your model.
+
+## Learn More and Next Steps
+
+Once you have the sample project running, you can try the following:
+
+  - [Learn more about how the sample works](./LEARN_MORE.md#how-it-works)
+  - [Try it on your own data set](./LEARN_MORE.md#use-your-own-data)
+
+To find out more about building churn prediction models, check out the
+[user guide](https://dato.com/learn/userguide/churn_prediction/quick-start.html)
+or [API documentation](https://dato.com/products/create/docs/graphlab.toolkits.churn_predictor.html).
+
 
 ## Troubleshooting
 
 If you are having trouble, please [create a Github Issue](https://github.com/dato-code/sample-churn-predictor/issues/new)
 or start a discussion on the [user forum](http://forum.dato.com/).
+
+
+## Acknowledgments
+
+The online retail data set used in this sample was obtained from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Online+Retail).