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The Predict API classifies data by using a classification service created by the Train Prediction API.
The unclassified data set must have the same structure as the training data set. All columns must exist including the prediction field column. The prediction field column content must have the value specified as the emtpy_value parameter ("-1" by default).
Copy this URL into Tableau's Web Data Connector input:
in the top navigation pane to connect to data.
https://t.blockspring.com/bs/run-prediction-model-hp-iodFinish the setup steps to use Run Prediction Model on Provided Data in Tableau.
Finish the setup steps to use Run Prediction Model on Provided Data in Slack.
Run this function with a POST request to Blockspring.
Visit the node.js quickstart to get started fast.
Visit the php quickstart to get started fast.
Visit the python quickstart to get started fast.
Visit the ruby quickstart to get started fast.
Visit the r quickstart to get started fast.
Visit the javascript quickstart to get started fast.
Use this URL for webhooks. You'll want to make a POST request.
curl -H "Content-Type: application/json" -d "{ \"url\": , \"service_name\": , \"hp_iod_token\": }" "https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?"
var blockspring = require("blockspring");
blockspring.runParsed("run-prediction-model-hp-iod", { "url": , "service_name": , hp_iod_token: }, function(res) {
console.log(res.params);
});
var request = require("request");
request.post({
url: "https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?",
form: { "url": , "service_name": , hp_iod_token: }
},
function(err, response, body) {
console.log(JSON.parse(body));
});
<?php
$url = 'https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?';
$data = json_encode(array("url" => , "service_name" => , "hp_iod_token" => ));
// use key 'http' even if you send the request to https://...
$options = array(
'http' => array(
'header' => array("Accept: application/json", "Content-Type: application/json"),
'method' => 'POST',
'content' => $data,
),
);
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
var_dump(json_decode($result));
?>
<?php
require('blockspring.php');
var_dump(Blockspring::runParsed("run-prediction-model-hp-iod", array("url" => , "service_name" => , "hp_iod_token" => ))->params);
import json
import urllib2
req = urllib2.Request("https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?")
req.add_header('Content-Type', 'application/json')
data = { "url": , "service_name": , "hp_iod_token": }
results = urllib2.urlopen(req, json.dumps(data)).read()
print json.loads(results)
import blockspring
import json
print blockspring.runParsed("run-prediction-model-hp-iod", { "url": , "service_name": , "hp_iod_token": }).params
require 'rest_client'
response = RestClient.post 'https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?', JSON.dump({ "url" => , "service_name" => , "hp_iod_token" => }), :content_type => :json
puts JSON.load(response)
require 'blockspring'
puts Blockspring.runParsed("run-prediction-model-hp-iod", { "url" => , "service_name" => , "hp_iod_token" => } ).params
require 'rest_client'
response = RestClient.post 'https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?', JSON.dump({ "url" = , "service_name" = , "hp_iod_token" = }), :content_type => :json
puts JSON.load(response)
library('blockspring')
library('rjson')
print(blockspringRunParsed("run-prediction-model-hp-iod", list( "url" = , "service_name" = , "hp_iod_token" = ))$params)
<script src="https://code.jquery.com/jquery-1.10.1.min.js"></script>
<script src="https://cdn.blockspring.com/blockspring.js"></script>
<script>
blockspring.runParsed("run-prediction-model-hp-iod", { "url": , "service_name": , hp_iod_token: }, { "api_key": "" }, function(res){
console.log(res.params);
})
</script>
https://run.blockspring.com/api_v2/blocks/run-prediction-model-hp-iod?
Finish the setup steps to use Run Prediction Model on Provided Data in Code.