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Among all industries, the insurance domain has one of the largest uses of analytics & data science methods. This dataset are collected from insurance companies – what challenges are faced there, what strategies are used, which variables influence the outcome, etc. This is a classification problem. The data has 615 rows and 13 columns. Problem: Predict if a loan will get approved or not. Our aim from the project is to make use of pandas, matplotlib, & seaborn libraries from python to extract insights from the data and xgboost, & scikit-learn libraries for machine learning. Secondly, to learn how to hypertune the parameters using grid search cross validation for the xgboost machine learning model. And in the end, to predict whether the loan applicant can replay the loan or not using voting ensembling techniques of combining the predictions from multiple machine learning algorithms. This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in. In this course, we are solving a real life case study of Dream Housing Finance. The company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.
Why: Problem statement
To predict the loan eligibility process based on customer detail provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others.
How: Solution description
This project is proposed to predict if a loan will get approved or not by finding which variables influence the outcome using Classification algorithms.
First, the dataset is cleaned up to remove or replace missing values. The columns in the dataset are;
Unique Loan ID
Applicant married (Y/N)
Number of dependents
Applicant Education (Graduate/ Under Graduate)
Loan amount in thousands
Term of the loan in months
credit history meets guidelines
Urban/ Semi-Urban/ Rural
Loan approved (Y/N)
Splitting data for training and testing:
This dataset contains 641 customer records who applied for a loan. We split the training and testing data with a ratio of 7:3. That is 70% of data is for training and 30% is for testing. We used the train_test_split method from sklearn to split the data.
Training using Machine learning models:
We got the training accuracy of 80% by using KNeighborsClassifier and training accuracy of 98% by using DecisionTreeClassifier
How is it different from competition
We have undergone many approaches for data cleaning. Since we made our data perfectly clean, we attained maximum accuracy.
Who are your customers
Insurance companies, Banks, money lending companies and other organizations can use this project.