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Loan Approval Prediction
based on Machine
Learning Approach
B y I s l a m N a d e r
Agenda
• Motivation
• Problem statement
• Objectives
• Background
• Dataset specifications
• Machine Leaning prediction Model
• Decision Tree Classifier
• Logistic Regression
• Naïve Bayesian Classifier
• Experimental result
2
3
Motivation
4
Motivation
• Distribution of the loans is the core business part of almost every banks. The main portion
the bank’s
assets is directly came from the profit earned from the loans distributed by the banks.
• The prime objective in banking environment is to invest their assets in safe hands
• Today many banks/financial companies approves loan after a regress process of verification
and validation but still there is no surety whether the chosen applicant is the deserving
right applicant out of all applicants
• Through this system we can predict whether that particular applicant is safe or not and the
whole process of validation of features is automated by machine learning technique
5
Problem statement
6
Problem statement
• Loan Prediction is very helpful for employee of banks as well as for the applicant also. The
aim of this Search is to provide quick, immediate and easy way to choose the deserving
applicants. It can provide special advantages to the bank. The Loan Prediction System can
automatically calculate the weight of each features taking part in loan processing and on
new test data same features are processed with respect to their associated weight .
• A time limit can be set for the applicant to check whether his/her loan can be sanctioned
or not. Loan Prediction System allows jumping to specific application so that it can be
check on priority basis.
• This Search is exclusively for the managing authority of Bank/finance company, whole
process of prediction is done privately no stakeholders would be able to alter the
processing. Result against particular Loan Id can be send to various department of banks so
that they can take appropriate action on application.
• This helps all others department to carried out other formalities
7
Objectives
8
Objectives
• The primary goal of this search is to extract patterns from a common loan approved
dataset, and then build a model based on these extracted patterns, in order to predict the
likely loan defaulters by using classification data mining algorithms.
• The historical data of the customers will be used in order to do the analysis. Later on,
some analysis will also be done to find the most relevant attributes, i.e., the factors that
affect the prediction result the most.
9
Dataset specifications
Dataset specifications
Variable Description Data Type
Loan_ID Unique Loan ID String
Gender
Male/ Female String
Married Applicant married (Y/N) String
Dependents Number of dependents Number
Education Applicant Education (Graduate/ Under Graduate) String
Self_Employed Self employed (Y/N) String 10
This data set was collected from our Internal Workflow system for manual loan origination
Dataset specifications
11
Variable Description
Data Type
ApplicantIncome Applicant income in EGP Number
CoapplicantIncome
Coapplicant Income in EGP if Exsit Number
LoanAmount Loan amount in thousands Number
Loan_Term Term of loan in months Number
Credit_History credit history meets guidelines String
Property_Area Urban/ Semi Urban/ Rural String
Loan_Status Loan approved (Y/N) String
12
Decision Tree Classifier
Machine Leaning prediction Model
13
Decision Tree Classifier
• The basic algorithm of decision tree requires all attributes or features should be
discretized.
• Feature selection is based on greatest information gain of features.
• The knowledge depicted in decision tree can represented in the form of IF-THEN rules.
• This model is an extension of C4.5 classification algorithms described by Quinlan
•
14
Decision Tree Classifier
15
Model Implementation
16
Logistic Regression
Machine Leaning prediction Model
17
Logistic Regression
• Logistic regression is also called logistic model or logit regression. It takes in independent
features and returns output as categorical output.
• The probability of occurrence of an categorical output can also be found by logistic
regression model by fitting the features in the logistic curve.
• The Logistic Regression model can be replaced by the simpler Linear Regression model
when the output variable is taken to be continuous
18
Model Implementation
19
Naïve Bayesian Classifier
Machine Leaning prediction Model
20
Naïve Bayesian Classifier
• A Naive Bayes classifier is a probabilistic machine learning model that’s used for
classification task. The crux of the classifier is based on the Bayes theorem
• It can also be represented using a very simple Bayesian network. Naive Bayes classifiers
have been especially popular for text classification, and are a traditional solution for
problems such as spam detection
21
Model Implementation
22
Experimental result
23
Experimental result
•
Experimental result
Model Accuracy
Decision Tree 0.8323754789272031
Logistic Regression 0.8061941251596424
Naïve Bayesian 0.8030012771392082
Thank You.
Eslam Nader
+201113648381
Esalmnader@outlook.com

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Loan approval prediction based on machine learning approach

  • 1. Loan Approval Prediction based on Machine Learning Approach B y I s l a m N a d e r
  • 2. Agenda • Motivation • Problem statement • Objectives • Background • Dataset specifications • Machine Leaning prediction Model • Decision Tree Classifier • Logistic Regression • Naïve Bayesian Classifier • Experimental result 2
  • 4. 4 Motivation • Distribution of the loans is the core business part of almost every banks. The main portion the bank’s assets is directly came from the profit earned from the loans distributed by the banks. • The prime objective in banking environment is to invest their assets in safe hands • Today many banks/financial companies approves loan after a regress process of verification and validation but still there is no surety whether the chosen applicant is the deserving right applicant out of all applicants • Through this system we can predict whether that particular applicant is safe or not and the whole process of validation of features is automated by machine learning technique
  • 6. 6 Problem statement • Loan Prediction is very helpful for employee of banks as well as for the applicant also. The aim of this Search is to provide quick, immediate and easy way to choose the deserving applicants. It can provide special advantages to the bank. The Loan Prediction System can automatically calculate the weight of each features taking part in loan processing and on new test data same features are processed with respect to their associated weight . • A time limit can be set for the applicant to check whether his/her loan can be sanctioned or not. Loan Prediction System allows jumping to specific application so that it can be check on priority basis. • This Search is exclusively for the managing authority of Bank/finance company, whole process of prediction is done privately no stakeholders would be able to alter the processing. Result against particular Loan Id can be send to various department of banks so that they can take appropriate action on application. • This helps all others department to carried out other formalities
  • 8. 8 Objectives • The primary goal of this search is to extract patterns from a common loan approved dataset, and then build a model based on these extracted patterns, in order to predict the likely loan defaulters by using classification data mining algorithms. • The historical data of the customers will be used in order to do the analysis. Later on, some analysis will also be done to find the most relevant attributes, i.e., the factors that affect the prediction result the most.
  • 10. Dataset specifications Variable Description Data Type Loan_ID Unique Loan ID String Gender Male/ Female String Married Applicant married (Y/N) String Dependents Number of dependents Number Education Applicant Education (Graduate/ Under Graduate) String Self_Employed Self employed (Y/N) String 10 This data set was collected from our Internal Workflow system for manual loan origination
  • 11. Dataset specifications 11 Variable Description Data Type ApplicantIncome Applicant income in EGP Number CoapplicantIncome Coapplicant Income in EGP if Exsit Number LoanAmount Loan amount in thousands Number Loan_Term Term of loan in months Number Credit_History credit history meets guidelines String Property_Area Urban/ Semi Urban/ Rural String Loan_Status Loan approved (Y/N) String
  • 12. 12 Decision Tree Classifier Machine Leaning prediction Model
  • 13. 13 Decision Tree Classifier • The basic algorithm of decision tree requires all attributes or features should be discretized. • Feature selection is based on greatest information gain of features. • The knowledge depicted in decision tree can represented in the form of IF-THEN rules. • This model is an extension of C4.5 classification algorithms described by Quinlan •
  • 17. 17 Logistic Regression • Logistic regression is also called logistic model or logit regression. It takes in independent features and returns output as categorical output. • The probability of occurrence of an categorical output can also be found by logistic regression model by fitting the features in the logistic curve. • The Logistic Regression model can be replaced by the simpler Linear Regression model when the output variable is taken to be continuous
  • 19. 19 Naïve Bayesian Classifier Machine Leaning prediction Model
  • 20. 20 Naïve Bayesian Classifier • A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task. The crux of the classifier is based on the Bayes theorem • It can also be represented using a very simple Bayesian network. Naive Bayes classifiers have been especially popular for text classification, and are a traditional solution for problems such as spam detection
  • 23. 23 Experimental result • Experimental result Model Accuracy Decision Tree 0.8323754789272031 Logistic Regression 0.8061941251596424 Naïve Bayesian 0.8030012771392082
  • 24.