Project is all about predicting the house price and whether a particular user will be eligible for loan or not. Project is entirely done in jupyter notebook with python language used numpy , pandas ,seeborn ,scipy and sklearn libraries . We have used two models to predict the house price details and the mortgage details. They are Lasso Regression – used for house price prediction. Random Forest Classifier – used for mortgage approval prediction.

Using lasso regression, we can predict the dependent variable ‘Sales price’ which is a continuous variable. Whereas random forest classifier will make decision trees even though it takes more space, it will provide better results.

House pricing depends on multiple factors like the Home size and usable space, condition of the house, location whether it is urban, semi urban or rural and the facilities it is going to provide like kitchen, number of bedrooms and garden.

Several factors include Gender, Education Qualification, Self Employed, Dependents and whether the person is married or not will effect the mortgage status. Also, the income of applicant, Loan amount, area of the property and credit history of the person are necessary to know if a certain person can get a loan or not.