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Loan Data
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    • Loan Data

    This dataset consists of more than 9,500 loans with information on the loan structure, the borrower, and whether the loan was pain back in full. This data was extracted from LendingClub.com, which is a company that connects borrowers with investors.

    Not sure where to begin? Scroll to the bottom to find challenges!

    Data dictionary

    VariableExplanation
    0credit_policy1 if the customer meets the credit underwriting criteria; 0 otherwise.
    1purposeThe purpose of the loan.
    2int_rateThe interest rate of the loan (more risky borrowers are assigned higher interest rates).
    3installmentThe monthly installments owed by the borrower if the loan is funded.
    4log_annual_incThe natural log of the self-reported annual income of the borrower.
    5dtiThe debt-to-income ratio of the borrower (amount of debt divided by annual income).
    6ficoThe FICO credit score of the borrower.
    7days_with_cr_lineThe number of days the borrower has had a credit line.
    8revol_balThe borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).
    9revol_utilThe borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).
    10inq_last_6mthsThe borrower's number of inquiries by creditors in the last 6 months.
    11delinq_2yrsThe number of times the borrower had been 30+ days past due on a payment in the past 2 years.
    12pub_recThe borrower's number of derogatory public records.
    13not_fully_paid1 if the loan is not fully paid; 0 otherwise.

    Source of dataset.

    Don't know where to start?

    Challenges are brief tasks designed to help you practice specific skills:

    • 🗺️ Explore: Generate a correlation matrix between the numeric columns. What columns are positively and negatively correlated with each other? Does it change if you segment it by the purpose of the loan?
    • 📊 Visualize: Plot histograms for every numeric column with a color element to segment the bars by not_fully_paid.
    • 🔎 Analyze: Do loans with the same purpose have similar qualities not shared by loans with differing purposes? You can consider only fully paid loans.

    Scenarios are broader questions to help you develop an end-to-end project for your portfolio:

    You recently got a job as a machine learning scientist at a startup that wants to automate loan approvals. As your first project, your manager would like you to build a classifier to predict whether a loan will be paid back based on this data. There are two things to note. First, there is class imbalance; there are fewer examples of loans not fully paid. Second, it's more important to accurately predict whether a loan will not be paid back rather than if a loan is paid back. Your manager will want to know how you accounted for this in training and evaluation your model.

    You will need to prepare a report that is accessible to a broad audience. It will need to outline your motivation, analysis steps, findings, and conclusions.

    Hierarchical Clustering

    Information about the data

    import pandas as pd

loan_data = pd.read_csv("loan_data.csv")
loan_data.head()
    loan_data["not.fully.paid"].value_counts()
    # Information about the data
loan_data.info()

    We can see that the data has 14 columns of numerical types except purpose which is object and gives the textual description of the purpose of the load. This column will not be included in our analysis.

    Preprocessing the data

    # Compute percentage of missing values in each column 
percent_missing = round(100*(loan_data.isnull().sum())/len(loan_data), 2)
percent_missing

    Drop unwanted columns

    # Remove irrelevant columns
cleaned_data = loan_data.drop(['purpose', 'not.fully.paid'], axis=1)
cleaned_data.info()
    cleaned_data.head()
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