Home Equity Loan Classification Analysis We Begin By Loading ✓ Solved

Home Equity Loan Classification Analysiswe Begin By Loadin

We begin by loading relevant R libraries needed for analysis. The hashtag symbol (#) is used to denote comments in the R code.

Next, we read in the data. Ensure that the dataset file has been uploaded to your notebook before attempting to read the data. There are 5,960 observations (rows) and 13 variables (columns) in this dataset. The variables include:

  • BAD: Customer default on loan, "Yes" or "No"
  • LOAN_AMT: Amount of home equity loan
  • MORTGAGE_REMAIN: Amount owed on home mortgage
  • PROPERTY_VALUE: Value of the property from which equity is borrowed
  • REASON: Customer's stated reason for the home equity loan
  • JOB: Customer's job title
  • YRS_JOB: Duration at the current job
  • DEROG: Number of derogatory marks on credit history
  • DELINGQ: Number of delinquent marks on credit history
  • OLDEST_CRED_LINE_MTHS: Age of customer's oldest line of credit in months
  • NUM_RECENT_INQ: Number of recent inquiries on credit history
  • NUM_CRED_LINES: Number of credit lines
  • DEBT_INC_RATIO: Ratio of customer's debt to income

The response variable is BAD and indicates whether the home equity loan customer defaults on the loan. The variable is binary, and R has correctly categorized it as a factor. There is missing data in almost all variables, and it must be addressed before splitting the dataset.

Visualize the relationships between each of the potential predictor variables and the default variable (BAD) to determine their respective impacts on loan defaults.

Paper For Above Instructions

The classification of home equity loans is an essential analysis in the financial world as it provides insights into customer behavior and the risks associated with lending. This paper utilizes R programming to conduct a comprehensive analysis of a dataset consisting of 5,960 observations and 13 variables concerning home equity loans.

Initially, we load the necessary R libraries that facilitate data analysis and visualization. Libraries such as tidyverse, caTools, rpart, and rpart.plot are critical for data manipulation and for constructing classification trees, which are vital tools for predicting defaults on loans.

Following the installation of the pertinent libraries, we import the dataset using the read.csv function. Upon executing this command, we get an overview of our dataset displaying 5,960 observations across 13 variables. Each variable has its own characteristics, including whether customers default on their loans (BAD), the amount of the loan (LOAN_AMT), the remaining mortgage balance (MORTGAGE_REMAIN), the property value (PROPERTY_VALUE), and other customer attributes.

Understanding the structure of the data is critical. After importing the dataset, we examine the structure and summary statistics to identify patterns and potential issues, including missing data, which is prevalent across all variables. The question arises whether this missingness itself might serve as a predictor of defaults. For instance, customers missing values in their debt-to-income ratio (DEBT_INC_RATIO) may suggest they have no debt, potentially indicating a lower risk of default. Thus, strategies for handling missing data must be carefully evaluated.

We utilize various visualization techniques to explore the relationships between the predictor variables and the response variable (BAD). This allows for a visual inspection of patterns and potential correlations that may exist. For example, boxplots and histograms can illustrate whether smaller loan amounts or remaining mortgage balances correlate with a higher frequency of defaults. Thus, we create plots such as ggplot(train, aes(x=BAD, y=LOAN_AMT)) + geom_boxplot() to see how loan amounts distribute among those who defaulted versus those who did not.

After inspecting these visualizations, we conclude that smaller loan amounts are indeed correlated with a higher likelihood of default. Similarly, the data reveals that lower property values and fewer years at current jobs also exhibit tendencies towards higher default rates. Notably, specific categorical variables, like REASON for taking out the loan, appear to differentiate between customers more likely to experience defaults compared to others.

Addressing the missing data problem is crucial before training predictive models. However, for this analysis, we opt to proceed directly with the classification task. We implement a classification tree model (`rpart`) to predict loan default status based on the identified predictors. The model is trained with a 70/30 train-test split conducted using the sample.split function.

On evaluating our classification tree model, we find that it achieves an accuracy of around 85% on the training set, which is a substantial improvement over a naive model that classifies everyone as non-defaulting based merely on majority class logic. After conducting predictions on the test dataset, we assess the model's effectiveness by calculating accuracy and examining discrepancies in predictions.

Finally, accuracy metrics provide a quantitative evaluation of our model's performance. Despite achieving a solid accuracy rate of 85%, it is crucial that we reflect on the importance of addressing missing data effectively and recalibrating our model as necessary. Overall, the analysis highlights the significant predictors of loan defaults, including loan amount, property value, years employed, derogatory marks, and debt-to-income ratio.

References

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