public ActionResult Index(Models.CreditApplication application)
{
//Open HttpClient
using (var client = new HttpClient())
{
//Populate data structure that will be posted to Azure ML Service
ScoreData scoreData = new ScoreData()
{
FeatureVector = new Dictionary <string, string>()
{
{ "Checking account", application.CheckingAccount },
{ "Duration in months", application.DurationInMonths },
{ "Credit history", application.CreditHistory },
{ "Purpose", application.Purpose },
{ "Credit amount", application.CreditAmount },
{ "Savings account/bond", application.SavingsAccountBonds },
{ "Present employment since", application.PresentEmploymentSince },
{ "Installment rate in percentage of disposable income", application.InstallmentRate },
{ "Personal status and sex", application.PersonalStatusAndSex },
{ "Other debtors", application.OtherDebtorsGuarantors },
{ "Present residence since", application.PresentResidenceSince },
{ "Property", application.Property },
{ "Age in years", application.AgeInYears },
{ "Other installment plans", application.OtherInstallmentPlans },
{ "Housing", application.Housing },
{ "Number of existing credits", application.NumberOfExistingCredits },
{ "Job", application.Job },
{ "Number of people providing maintenance for", application.NumberOfPeopleBeingLiableFor },
{ "Telephone", application.Telephone },
{ "Foreign worker", application.ForeignWorker },
},
GlobalParameters = new Dictionary <string, string>()
{
}
};
//Encapsulate request and make it ready for posting
ScoreRequest scoreRequest = new ScoreRequest()
{
Id = "score00001",
Instance = scoreData
};
// Replace this with the API key for the web service
const string apiKey = "FvTvOPjO+4PHQnO4sXgdMlvLQfkVvsSX8T5c0QLnRHdZZ1Um2inymPNoiRV/oNFQ+uu64Si4vr+2PhFbT4WXzg==";
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
//Set the Web Service address in Azure ML
client.BaseAddress = new Uri("https://ussouthcentral.services.azureml.net/workspaces/f0be5175783044cbba05e008f3f58135/services/b494338b3ac24356ae69d864200288bb/score");
//Send the request as JSON to web service and get the response
HttpResponseMessage response = client.PostAsJsonAsync("", scoreRequest).Result;
//If response is success
if (response.IsSuccessStatusCode)
{
//Get unformatted result set from Azure ML
string result = response.Content.ReadAsStringAsync().Result;
string[] resultArray = result.Split(',');
//Get the result data from ML and set to model, 1/true => Low Credit Risk / 2/false => Hight Credit Risk
application.Result = resultArray[20].Replace('"', ' ').Trim() == "1" ? "Kredi Vermeye Uygun" : "Kredi İçin Riskli";
ViewData["CreditResult"] = application.Result;
ViewData["ResultText"] = "Kredi İsteğiniz Başarılı Bir Şekilde İşleme Konuldu. İşlem Sonucu: ";
}
else
{
ViewData["ResultText"] = "İşlem Başarısız, hata kodu: " + response.StatusCode;
}
}
return(View(application));
}
public ActionResult Index(FormCollection formCollection)
{
//Initilize String List that will hold data that will posted from form
List <String> formData = new List <string>();
//Populate String List with data from form
foreach (string _formData in formCollection)
{
formData.Add(formCollection[_formData]);
}
//Open HttpClient
using (var client = new HttpClient())
{
//Populate data structure that will be posted to Azure ML Service
ScoreData scoreData = new ScoreData()
{
FeatureVector = new Dictionary <string, string>()
{
{ "Status of checking account", formData[0] },
{ "Duration in months", formData[1] },
{ "Credit history", formData[2] },
{ "Purpose", formData[3] },
{ "Credit amount", formData[4] },
{ "Savings account/bond", formData[5] },
{ "Present employment since", formData[6] },
{ "Installment rate in percentage of disposable income", formData[7] },
{ "Personal status and sex", formData[8] },
{ "Other debtors", formData[9] },
{ "Present residence since", formData[10] },
{ "Property", formData[11] },
{ "Age in years", formData[12] },
{ "Other installment plans", formData[13] },
{ "Housing", formData[14] },
{ "Number of existing credits", formData[15] },
{ "Job", formData[16] },
{ "Number of people providing maintenance for", formData[17] },
{ "Telephone", formData[18] },
{ "Foreign worker", formData[19] },
},
GlobalParameters = new Dictionary <string, string>()
{
}
};
//Encapsulate request and make it ready for posting
ScoreRequest scoreRequest = new ScoreRequest()
{
Id = "score00001",
Instance = scoreData
};
// Replace this with the API key for the web service
const string apiKey = "6C4v+O+N7HsaR/fFnzzI9U8yAlTrbvKPbAFvUg624+wmc0ayQRk4gY74egfHzLFQD5sIgB05nOxgwbBRLQgEVg==";
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
//Set the Web Service address in Azure ML
client.BaseAddress = new Uri("https://ussouthcentral.services.azureml.net/workspaces/557ef824663045bbaffe6a0b37abe781/services/ba11b13608d04063a7fbda494dca5ef3/score");
//Send the request as JSON to web service and get the response
HttpResponseMessage response = client.PostAsJsonAsync("", scoreRequest).Result;
//If response is success
if (response.IsSuccessStatusCode)
{
//Get unformatted result set from Azure ML
string result = response.Content.ReadAsStringAsync().Result;
string[] resultArray = result.Split(',');
//Get the result data from, 1 => Low Credit Risk / 2 => Hight Credit Risk
ViewData["result"] = resultArray[20].Replace('"', ' ').Trim() == "1" ? "Kredi Riski Düşük" : "Kredi Riski Yüksek";
}
else
{
ViewData["result"] = "İşlem Başarısız, hata kodu: " + response.StatusCode;
}
}
return(View());
}
