Loan Approval Prediction using Machine Learning

Last Updated : 23 Jul, 2025

Loans are a major requirement of the modern world. By this alone, banks receive a major portion of the total profit. It is beneficial for students to manage their education and living expenses, and for individuals to purchase various luxuries, such as houses and cars. But when it comes to deciding whether the applicant's profile is relevant to be granted with loan or not. Banks have to look after many aspects.

So, here we will be using machine learning algorithms to ease their work and predict whether the candidate’s profile is relevant or not, using key features like Marital Status, Education, Applicant Income, Credit History, etc.

Loan Approval Prediction using Machine Learning

You can download the used data by visiting this link.

The dataset contains 13 features:

1	Loan	A unique id
2	Gender	Gender of the applicant Male/female
3	Married	Marital Status of the applicant, values will be Yes/ No
4	Dependents	It tells whether the applicant has any dependents or not.
5	Education	It will tell us whether the applicant is Graduated or not.
6	Self_Employed	This defines that the applicant is self-employed i.e. Yes/ No
7	ApplicantIncome	Applicant income
8	CoapplicantIncome	Co-applicant income
9	LoanAmount	Loan amount (in thousands)
10	Loan_Amount_Term	Terms of loan (in months)
11	Credit_History	Credit history of individual's repayment of their debts
12	Property_Area	Area of property i.e. Rural/Urban/Semi-urban
13	Loan_Status	Status of Loan Approved or not i.e. Y- Yes, N-No

Importing Libraries and Dataset

Firstly we have to import libraries:

Pandas - To load the Dataframe
Matplotlib - To visualize the data features i.e. barplot
Seaborn - To see the correlation between features using heatmap

Once we imported the dataset, let's view it using the below command.

Output:

👁 Image

Dataset Loaded

Data Preprocessing and Visualization

Get the number of columns of object datatype.

Output :

Categorical variables: 7

As Loan_ID is completely unique and not correlated with any of the other column, So we will drop it using .drop() function.

Visualize all the unique values in columns using barplot. This will simply show which value is dominating as per our dataset.

Output:

👁 stats

Barplot

As all the categorical values are binary so we can use Label Encoder for all such columns and the values will change into int datatype.

Again check the object datatype columns. Let's find out if there is still any left.

Output :

Categorical variables: 0

Output:

👁 Image

Heatmap

The above heatmap is showing the correlation between Loan Amount and ApplicantIncome. It also shows that Credit_History has a high impact on Loan_Status.

Now we will use Catplot to visualize the plot for the Gender, and Marital Status of the applicant.

Output:

👁 Image

Catplot

Now we will find out if there is any missing values in the dataset using below code.

Output:

👁 Screenshot-2025-04-02-121509

As there is no missing value then we must proceed to model training.

Splitting Dataset

Output:

((358, 11), (240, 11), (358,), (240,))

Model Training and Evaluation

As this is a classification problem so we will be using these models :

To predict the accuracy we will use the accuracy score function from scikit-learn library.

Output :

Accuracy score of RandomForestClassifier = 98.04469273743017
Accuracy score of KNeighborsClassifier = 78.49162011173185
Accuracy score of SVC = 68.71508379888269
Accuracy score of LogisticRegression = 80.44692737430168

Prediction on the test set:

Output :

Accuracy score of RandomForestClassifier = 82.5
Accuracy score of KNeighborsClassifier = 63.74999999999999
Accuracy score of SVC = 69.16666666666667
Accuracy score of LogisticRegression = 80.83333333333333

Random Forest Classifier is giving the best accuracy with an accuracy score of 82% for the testing dataset. And to get much better results ensemble learning techniques like Bagging and Boosting can also be used.