Score Customer Propensity to Buy Mobile Home Policy

Your model is deployed in a secure sandbox. What now?

Train the model
You can train the model with either the provided sample training data or your own custom data.
Score Your Data
Submit your data to the trained model to be scored. You can do it by submitting a data file or interactively.
Customize this Model
Discuss this Model
Train the Model
The easiest and quickiest way to train the model is to click on one of the "Train Now" buttons! It will train the model with the sample dataset.
Train the model with sample training dataset
Train Now

Note:
Your model is not yet trained. You have to train the model before you can score.

Note:
Prediction is the target attribute for prediction. It is the last column in the CSV files.

Train the model with your own data. Follow these three simple steps:
Step 1:  
Start with Sample Training File as a template. The file conforms to the model's Data Dictionary
Step 2:   Customize with your own data.
Step 3:   Upload the file.
Upload File

Note:
Your model is not yet trained. You have to train the model before you can score.

Score Data
The easiest and quickest way to score some data is to click on "Score Now" button!
Here's a sample data set. It conforms to the model's data dictionary
Score Now

Note:
Your model is not yet trained. You have to train the model before you can score.

Note:

After the uploaded file is scored, it will be returned with two additional columns:
Prediction and Probability. They are the last two columns in the file.

Interactive Scoring Form
Add/Delete Rows Will Buy Mobile Home Policy? Probability? Subtype Houses Avg Household Size Ave Age Customer Type Roman Catholic Protestant Other Religion No Religion Married Living Together Other Relation Singles Household w/o Children Household w Children High Level Education Medium Level Education Lover Level Education High Status Entrepreneur Farmer Middele Management Skilled Laborer Unskilled Laborer Social Class A Social Class B1 Social Class B2 Social Class C Social Class D Rented House Home Owners 1 Car 2 Cars No Car NHS Private Health Insurance 30 30 - 45 45-75 75-122 123 Avg Income Purchasing Power Class Contribution private third party insurance Contribution third party insurance (firms) Contribution third party insurane (agriculture) Contribution car policies Contribution delivery van policies Contribution motorcycle/scooter policies Contribution lorry policies Contribution trailer policies Contribution tractor policies Contribution agricultural machines policies Contribution moped policies Contribution life insurances Contribution private accident insurance policies Contribution family accidents insurance policies Contribution disability insurance policies Contribution fire policies Contribution surfboard policies Contribution boat policies Contribution bicycle policies Contribution property insurance policies Contribution social security insurance policies Number of private third party insurance 1 - 12 Number of third party insurance (firms) Number of third party insurane (agriculture) Number of car policies Number of delivery van policies Number of motorcycle/scooter policies Number of lorry policies Number of trailer policies Number of tractor policies Number of agricultural machines policies Number of moped policies Number of life insurances Number of private accident insurance policies Number of family accidents insurance policies Number of disability insurance policies Number of fire policies Number of surfboard policies Number of boat policies Number of bicycle policies Number of property insurance policies Number of social security insurance policies
Add / Delete     33 1 4 2 8 0 6 0 3 5 0 4 1 1 8 2 2 6 0 0 1 2 6 1 0 2 1 5 3 1 8 8 1 1 8 1 3 3 3 0 0 3 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Add / Delete     6 1 3 2 2 0 5 0 4 5 2 2 1 4 5 5 4 0 5 0 0 4 0 0 4 3 0 2 1 3 6 9 0 0 7 2 1 1 5 4 0 6 8 2 0 0 6 0 4 0 0 0 0 0 3 0 0 0 4 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0
Add / Delete     39 1 3 3 9 1 4 2 3 5 2 3 2 3 6 2 4 4 2 1 1 3 2 2 1 1 5 2 1 1 8 6 2 2 6 3 2 4 3 1 0 3 5 2 0 0 6 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
Add / Delete     9 1 2 3 3 2 3 2 4 5 4 1 2 4 4 2 4 4 2 1 1 5 1 2 3 1 3 2 2 3 6 7 2 1 7 2 2 5 3 1 0 4 4 2 0 0 5 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Add / Delete     31 1 2 4 7 0 2 0 7 9 0 0 0 6 3 0 0 9 0 0 0 2 4 4 0 0 0 7 2 9 0 7 2 0 9 0 5 4 0 0 0 3 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Submit your own csv file with the data to be scored.
Note:
The csv file must conform to the data specification used for training the model. Therefore, we suggest that you follow these three simple steps:
Step 1:   Start with this Sample File as template.
Step 2:   Customize with your own data.
Step 3:   Submit the file below.
Upload File

Note:
Your model is not yet trained. You have to train the model before you can score.

Note:
After the uploaded file is scored, it will be returned with two additional columns:
Prediction and Probability. They are the last two columns in the file.
You can submit data to be scored programtically through the REST-based Service APIs.
For the API specification and details on how to develop applications using them please refer to the Developer's Guide. Note that you will need your personal session key to use with the APIs. Here's your session key:
No Session Key available. Train the model to get the session key.

Note:
For your convenience here's a sample Python application.