Bank Loan Patterns With Data Science

Posted on Jun 10, 2022

Introduction of Data Science in the Financial Industry

Data Science is wildly adapted in financial industry. Nowadays consumers can take loans through different financing platforms banks, financial firms etc. Bank enables to browse new consumer loan applications containing the applicant’s age, gender, income, employment status, number of dependents and other self-reported personal information, in order to make determinations as to which condition will leads to approved loans.

The goal was to use basic information about customers for exploration data analysis to the process of performing initial investigations on data to discover patterns, spot outliers, and test hypotheses with summary statistics and graphical representations.


The dataset is of a Bank that deals with granting home loans in urban, semi-urban, and rural areas. It consists of the following variables: ID, Person Age, Gender, Regional Area, Person’s Income, Self Employed Status, Married Status, Number of Dependents, Education Status, Loan Approved Status.

Factors affecting the loan acceptance decision

Person’s Income: A higher income increases the likelihood of receiving a loan.

Age: Young age person will have better chance of loan approval.

Regional Area: Living place increase trust level to provide loan.

Employment Status: Employed person has good chance to loan approval.

Education: Higher qualifications increases one's chances of landing a high-paying job and in result will earn more money.

Here is a list of other factors:

Loan Analysis Process

Loading the Data sets

  • Importing Libraries - libraries like pandas, numpy, seaboarn, matplotlib
  • Loading and show the dataset - loading bank loan data set file

Exploring the Train Data sets

  • Check the dataset by rows and columns - know the structure of data set
  • See the features in the Dataset - find the Data Type and number of observations in each column

Analyzing Variables in the Data set

  • 69% of the loan applications in the data got approved while 31% applications were rejected.
  • Loan Status bar chart

data science

Analyzing the categorical variables in the data

  • In the data set, 50 percent of the applicants are male, and 50 percent are females.
  • Most of the applicants are from Inner City area (45%), followed by Town area (28%), Rural areas (16%), Suburban areas (10%).
  • 65% of the applicants are Self Employed
  • 46% of the applicants are married.
  • 75% of the applicants are graduates.
  • 69% of the applicants have loan approved status.


data science

  • The Average of applicant’s income is $27936.07
  • The Average applicant's age of loan approval is between 40 year to 50 year
  • 43% of the applicants have zero dependents.


The loan is analyzed in this project on basis of applicant's different aspects and it is also extends to approval rate applications. I found that the living area of applicant is important with individual's employment status for loan approval. In addition, education of individual is a good indicator of the application pool of the approvals. Another beneficial factor is marriage status, married applicants have a higher chance of loan approval. By this marriage status benefit, another supportive object is number of dependents on the applicant. Less dependents is a good and supportive factor in loan approval success.

The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


About Author

Anupam Sharma

Experienced including leading, organizing, contributing and managing ability in a manner towards the growth of computer education to achieve the ultimate level. An analytical minded data scientist, eager to contribute my abilities in qualitative modeling and experimentation to...
View all posts by Anupam Sharma >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI