Loan Default Prediction Using MATLAB: Analyzing LendingClub Data Case Study

Exploring Logistic Regression and Random Forest Models in Loan Analysis

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Introduction: Loan Default Prediction Using MATLAB: Analyzing LendingClub Data

In the last few years lending companies are getting improved and grown rapidly over time. These companies are getting more and more customers compared to the banks and other loan departments. The companies are a great alternative to the banking sectors and the customers have more options available than before. LendingClub is an American company or industry that is lending company and it is getting famous worldwide for its reputation and the revenue gained by the company. But with the growth and improvements the chances of getting back the money or the loan repayments are getting lower and the risk of the loss is also getting higher than before. So, in the coursework, the main aim of the project is to find a predicted value of the loan repayment by the customer of the company to minimize the risk of facing a loss against the given loans.

Also, to ensure and make a prediction of those customers who are going to pay back their money. In this coursework, there are some datasets of the company and these datasets are containing with customer data along with some variables such as customer details, loan details, loan repayment status, time interval loan amount, etc. From the given datasets there is be a prediction of chances to get the loan money back. The overall process is done by using the MATLAB and through some phases such as data cleaning, data processing, normalizing, and transforming of data.

Methodologies

The techniques used to import the data, clean data, and apply models to analyze the dataset are all described in depth in this section and also discusses the underlying ideas of these models and highlight their advantages and disadvantages.

Data Import and Cleaning

Using MATLAB to import the provided.csv files containing the LendingClub dataset. The dataset includes data on a variety of loan characteristics, including loan amount, period, and rate of interest, credit rating, and loan status. By deleting superfluous variables, dealing with missing values, and transforming category variables into dummy variables, the datasets were cleaned (Masini et al. 2023).

Logistic Regression Model

Using the LendingClub dataset, logistic regression is used to predict loan default. A statistical technique that is frequently used to represent binary outcomes is logistic regression. In this instance, by modeling the loan qualities as a factor of the chance of default. Many benefits of logistic regression include its simplicity in interpretation and ability to handle both categorical and continuous data. It does, however, presuppose linearity between both the predictor and the outcome's log chances.

Random Forest Model

Using LendingClub data and the random forest model, it is possible to forecast loan default. A lot of decision trees are built using the random forest ensemble learning technique during the training phase, and the approach produces the class that represents the median of the class of the different trees. Random forests provide a number of benefits, including the ability to manage missing values and non-linear correlations between predictors and outcomes. Yet, they could overfit the data and be computationally demanding.

Model Assessment

Using the included testData.csv file, assess the effectiveness of the regression models and randomized forest models. For each model, the computing of the matrix, accuracy, accuracy, memory, and F1 score is done. To assess the effectiveness of the models, the ROC curves were also employed (Pal et al. 2023).

Main findings

In this part, the calculated values or the predicted values are given and the overall presentation of the estimated results for each of the models is given and discussed about them.

Figure 1: Function for accuracy configuration

The MATLAB function used in the given code may build a confusion matrix to determine how accurate a model for binary classification is. The comfort function accepts two input parameters as input: y or that, which stand for realized and anticipated outcomes, respectively.

Champing Sun has been recognized as the code's creator. The parameter TN is initially initialized to 0. The function then goes on to count each of the confusion matrix's four components, which is a matrix with dimensions 2x2 that represents the effectiveness of the binary categorization model. The code assumes both of the input parameters are variables that are binary, which are limited to having a value of 0 or 1. Then, using MATLAB syntax, it sets the integers of y or that to arrays containing 1s and 0s (Ashtiani et al. 2023).

After that, the function iterates across the length of y or evaluates each observation using the values of y and that that correspond to it. The observation is regarded as a genuinely positive and the associated component of the matrix of confusion is increased if both y(i) and yhat(i) are 1. The result is a genuine negative and the associated element is once more increased if both y(i) and yhat(i) are 0.

The result is a false positive if y(i) was 0 and yhat(i) is 1, and a false negative if y(i) is 1 but yhat(i) is 0. The confusion matrix's related elements are then appropriately increased.

The function then divides the total number of observations by the sum of the diagonal of the matrix of confusion to determine the classification model's accuracy. A 2x2 matrix of confusion, ccnf_mat, or the accuracy, accl, are the results of the function (Costola et al. 2023).

Figure 2: Configuration of the elements

The provided code is a script written in MATLAB that primarily counts the four components of a matrix of confusion and computes the binary classification model's accuracy rate. Two arrays, y and that, which stand for the actual and anticipated binary outputs, respectively, are initialized in the script's first section. These arrays are 1x4 in size and hold binary values either 1s or 0s. The script then loops over the length of y or evaluates each observation using the values of y and that that correspond to it. Determining how the numbers in y and hat are 1 or 0, the code utilizes conditional statements to decide whether to classify each observation. The result is a genuine positive if both of the values are 1, and a true negative if both numbers are 0. The result of the experiment is a bogus positive if y(i) is 0 when that(i) is 1, and a false negative when y(i) is 1 when that(i) is 0. The confusion matrix's related elements are then appropriately increased (Ghaffari Gol Afshani et al. 2023).

The second section of the script creates the confusion matrix using the four calculated matrix elements, TP, FP, FN, and TN, and then uses the MATLAB confusion chart tool to show it with the correct variable s or row s. The classification model's accuracy rate is then determined by adding the actual positive rate to the true negative rates and then dividing the result by the overall number of observations. With the MATLAB fprintf function, the accuracy is shown to the consumer with a precision of two decimal places. In order to summaries the model's classification performance, this MATLAB script creates a confusion matrix from two binary arrays reflecting the actual and predicted binary outputs. Additionally, it figures out the model's accuracy rate and displays it to the terminal (Bumin et al. 2023).

Figure 3: Accuracy Score

This is the accuracy score of the project and the output of the MATLAB code. The accuracy score includes some values such as acc, conf_mat, FN, FP, i, TN, TP, y, and that and all the values with respect to the parameters are also available in the given figure.

Figure 4: Confusion matrix

The image in the section above showcases the “2*2” table that has all of the values in this regard. Some of these are the actual positive, and actual negative and many others for that matter.

Figure 5: Dataset configuration

The offered code is a MATLAB script that does preliminary analysis or regression modelling using the Returns Predictability dataset. The code's initial responsibility is to analyses the dataset in rudimentary fashion. It examines the covariance structure including both the response variable and other predictors. The analysis is followed by conclusions. Unfortunately, the precise analysis done and the conclusions reached are not included in the code (Huang et al. 2023).

In the second job, the program estimates the Ndel utilizing the training sample using three models of regression (OLS, best group, and ridge regression). Using the testing data, the algorithm also calculates the remaining Normal Error (RSE) of each model and compares them. The given data, Ret_pred, has been divided into sets for training and testing, with the data used for training being recorded in a variable d TrainData and kept in a MAT file. In conclusion, the Returns of the predictability dataset are subjected to initial evaluation and regression modeling tasks using this MATLAB script. While applying three alternative models to estimate Ndel by computing the coefficient of variation (RSE) using testing data, the preliminary analysis verifies the covariance pattern of the predictors with the response variable. The given data has been divided into sets for training and testing, with the data used for training being recorded in a MAT file for later usage (Sawwalakhe et al. 2023).

Figure 6: Function configuration

The functions that have been taken into consideration in fulfilling all of the necessary requirements have been showcased with the help of this above picture. The aspect of their execution has also been displayed in this regard.

Figure 7: Training dataset

The dataset showcased by way of the picture above is representative of the training dataset which has been taken into account for the purpose of fulfilling the necessary objectives (Lestari et al. 2023).

Figure 8: Setting of the Values

The function d “confMat () pertains to the “MATLAB” code which has been utilized to properly calculate the “confusion matrix” as well as the rate of accuracy for the issues regarding “binary” classification. The very input arguments regarding this function are the output realized and the output forecasted and both of these are “binary” variables. The output pertaining to this function incorporates the “confusion matrix”, which contains the quantity of the “true positives”, “false positives”, “true negatives”, and “false negatives” respectively along with the rate of accuracy. This has been calculated in the form of the sum of “TP”, and “TN” divided by “total length” pertaining to the output (Bitetto et al. 2023).

The value of accuracy is “0.80” in this regard. This function starts its operations by initializing the variables which get used for the counting of “TP”, “TN”, “FN”, and “FP” pertaining to the “confusion matrix”. Furthermore, all these variables have been set to “0” at the very beginning of this function. The forecasted and the realized output has then been set to the fixed values for the purpose of testing. The “for loop” is utilized for reiterating through every element pertaining to the realized output as well as the forecasted output. The statement d “if-else” inside the loop is also utilized for determining all of the values regarding “TP”, “TN”, “FP”, and “FN” respectively (Zhou et al. 2023).

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Conclusions

The knowledge gained from this coursework is helpful in forecasting loan defaults using actual LendingClub data. Now to understand the significance of preprocessing data, choosing suitable models, and measuring model performance using various metrics. Initially, the data must be cleaned up and put through a number of preparation steps, such as addressing missing values, changing variables, and normalizing the data. Using the training dataset, then trained other models, such as random forests, logistical decision trees, logistic regression, and a support vector machine. Then, each model's performance is assessed using a variety of criteria, including precision, recall, precision, and F1 score.

The performance of our models is also visualized using a confusion matrix, and it was shown that the random forest model performed better than each of the other models when predicting defaulters and non-defaulters. Both lenders and debtors must be able to properly forecast loan default. Lenders may reduce their risk and guarantee that they only lend to borrowers who are more likely to repay their loans by accurately predicting defaulters. It can offer fair and open lending practices to borrowers, enhancing their access to finance. With the use of real-world data, this coursework has given students a practical grasp of how to create models that anticipate loan default. It has shown how crucial data preparation and picking the right models are for producing precise forecasts. By anticipating that the information and skills gained through this coursework may enable participants to contribute to the expanding field of information science and its utilization in the banking sector.

Reference

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