This project using Python language developed three Machine Learning models Logistic Regression, Support Vector Machine for Classification and Deep Learning and used the ensemble method to predict a Home Equity Loan is default or not. Then developed a Flask full stack web application for the model deployment. After the user input the loan data an API Call for prediction was sent to the server and the server by running the machine learning model got predictin result then rendered a Jinja Template with the result to the user.

The Home Equity Loan data is from Home Equity Loan Dataset.

This data set reports characteristics and delinquency information for 5,960 home equity loans.

A home equity loan is a loan where the obligor uses the equity of his or her home as the underlying collateral.

• Python web framework Flask and it's extensions Render_Template are used to render templates with specific data by using Jinja template library back to user.

• Python machine learning algrithems Logistic Regression, Support Vector Machine for Classification and Deep Learning are used.

• After Data Preprocessing process Feature Selection is done by using Correlation Matrix which keep top 15 features highly correlated to the target variable and these 15 features are less correclated to each other.

• Hyperparameters Tunning is done by using GridSearchCV. It allows to combine an estimator with a grid search preamble to tune hyper-parameters. The method picks the optimal parameter from the grid search and uses it with the estimator selected by the user.

• The Ensemble method is used for doing Model Prediction. it is a meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).

• DevHomeEquityPDModel.ipynb: This is the script for building the machine learning model.

• app.py: It is the Flask Server which has two routes. The root route / that will show the landing page, the other route called /predictonesample that will prvoide the DEFAULT prediction API.

• index.html: It is Jinja template HTML file structured by using bootstrap, it takes the prediction result string and displays the data in the appropriate HTML elements.

• modelprediction.html: It is the page accept users' input loan data and show the returned prediction result to users.

By sending requests from Brower to the Flask Server can get below results: