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Task 2: Explore dataset

Let's explore the dataset to understand each column.

  1. In the DATASETS page, observe the two datasets we will use for this tutorial.
  2. Click the loan_prediction_train.csv dataset and select the DETAILS option.
  3. Let’s take a quick overview of the columns of the training dataset:
  • The dataset consists of 13 columns which are as follows: Dataset columnsa. Loan_ID - A unique identifier for each loan application
    b. Gender - The gender of the applicant (Male/Female)
    c. Married - The marital status of the applicant (Yes/No)
    d. Dependents - The number of dependents the applicant has
    e. Education - The education level of the applicant (Graduate/Not Graduate)
    f. Self_Employed - Whether the applicant is self-employed (Yes/No)
    g. ApplicantIncome - The income of the loan applicant in numerical form
  • Continue scrolling to the right of the page to view the rest of the columns of the dataset: Dataset columnsh. CoapplicantIncome - The income of the co-applicant, if any
    i. LoanAmount - The loan amount requested
    j. Loan_Amount_Term - The term of the loan in months
    k. Credit_History - A numerical representation of the applicant's credit history (1 for good credit, 0 for bad credit)
    l. Property_Area - The type of property area (Urban, Semi-Urban, or Rural)
    m. Loan_Status - Whether the loan was approved (Y) or not (N)
  1. Return to the Datasets page.

Now that you understand each column of the dataset, in Task 3, we will learn how to set up the experiment from scratch.

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