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Chapter II. Literature Review
Sl. No . Title Author Independent Variable Dependent Variable Methodology Results Limitation 1 Predicting Loan Approval Using ML Nikhil Bansode, Adarsh Verma, Abhishek Sharma, Varsha Bhole Loan_ID: LP Gender: Female Married: No Dependents: 1 Education: Graduate Self_Employed: Yes ApplicantIncome: 5000 CoapplicantIncome: 4500 LoanAmount: 1000 Loan_Amount_Term: 360 Credit_History: 1 Property_Area: Rural Logistic Regression: 84.376 % Random Forest: 82.293 % Decision Tree: 71.873 % SVM- Support vector machines: 80.200 % K Neighbors- K-Nearest Neighbor: 76.700 % Collection of data's Selection of features Machine learning algorithms Operation of testing To acquire an accurate result, we used a variety of strategies to train our model. With a 30 percent testing set and 70 percent training set, the Logistic Regression Algorithm, the Decision Tree Algorithm, Random Forest Algorithm, SVM, and K Neighbors were implemented. Logistic regression, on the other hand, has the highest accuracy of all the algorithms
Loan_Status: Y 2 Predict Loan Approval in Banking System Machine Learning Approach for Cooperative Banks Loan Approval Amruta S. Aphale & Prof. Dr. Sandeep. R. Shinde Loan_ID: LP Gender: Male Married: Yes Dependents: 2 Education: Graduate Self_Employed: Yes ApplicantIncome: 6000 CoapplicantIncome: 3500 LoanAmount: 100 Loan_Amount_Term: 360 Credit_History: 1 Property_Area: Rural Loan_Status: Y Logistic Regression: 25%, Random Forest: 26%, Decision Tree: 22% and many more Analyze the dataset Operation of testing Machine learning algorithms 3 Approval Prediction using Machine Learning Algorithms Nitesh Pandey, Ramanand Gupta, Sagar Uniyal, Vishal Kumar Loan_ID: LP Gender: Male Married: Yes Logistic Regression: 27%, Random Forest: Data collection Analyze data Choosing a With the model trained, it needs to be tested. The data which we split during test trained
Self_Employed: Yes ApplicantIncome: 3000 CoapplicantIncome: 3500 LoanAmount: 100 Loan_Amount_Term: 360 Credit_History: 1 Property_Area: Rural Loan_Status: Y Random Forest Classifier Testing Machine learning Algorithms 5 Loan Approval Prediction based on Machine Learning Approach Kumar Arun, Garg Ishan, Kaur Sanmeet Loan_ID: LP Gender: Male Married: Yes Dependents: 2 Education: Graduate Self_Employed: Yes ApplicantIncome: 3000 CoapplicantIncome: Decision Trees Random Forest (RF) Support Vector Machine (SVM) Linear Models (LM) Neural Network (Nnet) Adaboost (ADB) Collection of data Feature selection Choosing a Model Testing Machine learning Algorithms
LoanAmount: 100 Loan_Amount_Term: 360 Credit_History: 1 Property_Area: Rural Loan_Status: Y
