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Loan approval prediction based on machine learning approach

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Loan Approval ML Validation and comparisons

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Loan approval prediction based on machine learning approach

  1. 1. Loan Approval Prediction based on Machine Learning Approach B y I s l a m N a d e r
  2. 2. Agenda • Motivation • Problem statement • Objectives • Background • Dataset specifications • Machine Leaning prediction Model • Decision Tree Classifier • Logistic Regression • Naïve Bayesian Classifier • Experimental result 2
  3. 3. 3 Motivation
  4. 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
  5. 5. 5 Problem statement
  6. 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
  7. 7. 7 Objectives
  8. 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.
  9. 9. 9 Dataset specifications
  10. 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. 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. 12 Decision Tree Classifier Machine Leaning prediction Model
  13. 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 •
  14. 14. 14 Decision Tree Classifier
  15. 15. 15 Model Implementation
  16. 16. 16 Logistic Regression Machine Leaning prediction Model
  17. 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
  18. 18. 18 Model Implementation
  19. 19. 19 Naïve Bayesian Classifier Machine Leaning prediction Model
  20. 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
  21. 21. 21 Model Implementation
  22. 22. 22 Experimental result
  23. 23. 23 Experimental result • Experimental result Model Accuracy Decision Tree 0.8323754789272031 Logistic Regression 0.8061941251596424 Naïve Bayesian 0.8030012771392082
  24. 24. Thank You. Eslam Nader +201113648381 Esalmnader@outlook.com