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I am trying to do some experiments with data science algorithms and looking for some sample datasets.

For example, I am looking for a sample patient dataset. For example, a dataset that each row is a patient (without identifiable information) and each column is a feature of that patient. For example a dataset with information about patients who checked for cancer and the result of the test.

I also like to have information about credit card applications. Each row is a person who applied for a credit card and information on the application result.

Where can I find this type of sample information?

1 Answer 1

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Stroke Prediction Dataset

Context

According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.

Attribute Information

  1. id: unique identifier
  2. gender: "Male", "Female" or "Other"
  3. age: age of the patient
  4. hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension
  5. heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease
  6. ever_married: "No" or "Yes"
  7. work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed"
  8. Residence_type: "Rural" or "Urban"
  9. avg_glucose_level: average glucose level in blood
  10. bmi: body mass index
  11. smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"*
  12. stroke: 1 if the patient had a stroke or 0 if not

Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Similar Datasets


A Credit Card Dataset for Machine Learning!

Context

Credit score cards are a common risk control method in the financial industry. It uses personal information and data submitted by credit card applicants to predict the probability of future defaults and credit card borrowings. The bank is able to decide whether to issue a credit card to the applicant. Credit scores can objectively quantify the magnitude of risk.

application_record.csv    
Feature name Explanation Remarks
ID Client number  
CODE_GENDER Gender  
FLAG_OWN_CAR Is there a car  
FLAG_OWN_REALTY Is there a property  
CNT_CHILDREN Number of children  
AMT_INCOME_TOTAL Annual income  
NAME_INCOME_TYPE Income category  
NAME_EDUCATION_TYPE Education level  
NAME_FAMILY_STATUS Marital status  
NAME_HOUSING_TYPE Way of living  
DAYS_BIRTH Birthday   Count backwards from current day (0), -1 means yesterday
DAYS_EMPLOYED Start date of employment Count backwards from current day(0). If positive, it means the person currently unemployed.
FLAG_MOBIL Is there a mobile phone  
FLAG_WORK_PHONE Is there a work phone  
FLAG_PHONE Is there a phone  
FLAG_EMAIL Is there an email  
OCCUPATION_TYPE Occupation  
CNT_FAM_MEMBERS Family size  
     
credit_record.csv    
Feature name Explanation Remarks
ID Client number  
MONTHS_BALANCE Record month The month of the extracted data is the starting point, backwards, 0 is the current month, -1 is the previous month, and so on
STATUS Status 0: 1-29 days past due 1: 30-59 days past due 2: 60-89 days overdue 3: 90-119 days overdue 4: 120-149 days overdue 5: Overdue or bad debts, write-offs for more than 150 days C: paid off that month X: No loan for the month

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