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Loan Eligibility Prediction using Machine Learning Models in Python
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Loan Approval Prediction using Machine Learning

Last Updated : 02 Apr, 2025
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LOANS are the major requirement of the modern world. By this only, Banks get a major part of the total profit. It is beneficial for students to manage their education and living expenses, and for people to buy any kind of luxury like houses, cars, etc. 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 : 

1LoanA unique id 
2GenderGender of the applicant Male/female
3MarriedMarital Status of the applicant, values will be Yes/ No
4DependentsIt tells whether the applicant has any dependents or not.
5EducationIt will tell us whether the applicant is Graduated or not.
6Self_EmployedThis defines that the applicant is self-employed i.e. Yes/ No
7ApplicantIncomeApplicant income
8CoapplicantIncomeCo-applicant income
9LoanAmountLoan amount (in thousands)
10Loan_Amount_TermTerms of loan (in months)
11Credit_HistoryCredit history of individual's repayment of their debts
12Property_AreaArea of property i.e. Rural/Urban/Semi-urban 
13Loan_StatusStatus 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
Python 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

data = pd.read_csv("LoanApprovalPrediction.csv")

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

Python 
data.head(5)

Output:

Dataset Loaded

Data Preprocessing and Visualization

Get the number of columns of object datatype.

Python 
obj = (data.dtypes == 'object')
print("Categorical variables:",len(list(obj[obj].index)))

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.

Python 
# Dropping Loan_ID column
data.drop(['Loan_ID'],axis=1,inplace=True)

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

Python 
obj = (data.dtypes == 'object')
object_cols = list(obj[obj].index)
plt.figure(figsize=(18,36))
index = 1

for col in object_cols:
  y = data[col].value_counts()
  plt.subplot(11,4,index)
  plt.xticks(rotation=90)
  sns.barplot(x=list(y.index), y=y)
  index +=1

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.

Python 
# Import label encoder
from sklearn import preprocessing
  
# label_encoder object knows how 
# to understand word labels.
label_encoder = preprocessing.LabelEncoder()
obj = (data.dtypes == 'object')
for col in list(obj[obj].index):
  data[col] = label_encoder.fit_transform(data[col])

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

Python 
# To find the number of columns with 
# datatype==object
obj = (data.dtypes == 'object')
print("Categorical variables:",len(list(obj[obj].index)))

Output : 

Categorical variables: 0
Python 
plt.figure(figsize=(12,6))

sns.heatmap(data.corr(),cmap='BrBG',fmt='.2f',
            linewidths=2,annot=True)

Output:

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.

Python 
sns.catplot(x="Gender", y="Married",
            hue="Loan_Status", 
            kind="bar", 
            data=data)

Output:

Catplot

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

Python 
for col in data.columns:
  data[col] = data[col].fillna(data[col].mean()) 
  
data.isna().sum()

Output:

Screenshot-2025-04-02-121509

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

Splitting Dataset 

Python 
from sklearn.model_selection import train_test_split

X = data.drop(['Loan_Status'],axis=1)
Y = data['Loan_Status']
X.shape,Y.shape

X_train, X_test, Y_train, Y_test = train_test_split(X, Y,
                                                    test_size=0.4,
                                                    random_state=1)
X_train.shape, X_test.shape, Y_train.shape, Y_test.shape

Output:

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

Model Training and Evaluation

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

  • KNeighborsClassifiers
  • RandomForestClassifiers
  • Support Vector Classifiers (SVC)
  • Logistics Regression

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

Python 
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression

from sklearn import metrics

knn = KNeighborsClassifier(n_neighbors=3)
rfc = RandomForestClassifier(n_estimators = 7,
                             criterion = 'entropy',
                             random_state =7)
svc = SVC()
lc = LogisticRegression()

# making predictions on the training set
for clf in (rfc, knn, svc,lc):
    clf.fit(X_train, Y_train)
    Y_pred = clf.predict(X_train)
    print("Accuracy score of ",
          clf.__class__.__name__,
          "=",100*metrics.accuracy_score(Y_train, 
                                         Y_pred))

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:

Python 
# making predictions on the testing set
for clf in (rfc, knn, svc,lc):
    clf.fit(X_train, Y_train)
    Y_pred = clf.predict(X_test)
    print("Accuracy score of ",
          clf.__class__.__name__,"=",
          100*metrics.accuracy_score(Y_test,
                                     Y_pred))

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.

Google Colab Link: click here


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Loan Eligibility Prediction using Machine Learning Models in Python

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    The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
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    In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
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    A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
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    Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
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    License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
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    Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
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    Natural Language Processing Projects

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    This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
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    Next Sentence Prediction using BERT
    Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
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    There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
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    Image Caption Generator using Deep Learning on Flickr8K dataset
    Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
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    Movie recommendation based on emotion in Python
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    Speech Recognition in Python using Google Speech API
    Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
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    Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
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    Human Activity Recognition - Using Deep Learning Model
    Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
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    Fine-tuning BERT model for Sentiment Analysis
    Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
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    BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
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    Sentiment Analysis with an Recurrent Neural Networks (RNN)
    Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
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    Building an Autocorrector Using NLP in Python
    Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
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    Python | NLP analysis of Restaurant reviews
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    Restaurant Review Analysis Using NLP and SQLite
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    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
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    Clustering Projects

    Customer Segmentation using Unsupervised Machine Learning in Python
    Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
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    Music Recommendation System Using Machine Learning
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    K means Clustering – Introduction
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    Image Segmentation using K Means Clustering
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