Mortgage Loan Prediction

Deployed app:

Application deployed on Heroku: link

Problem Statement:

Dream Housing Finance company deals in home mortgage loans. They have a presence across all urban, semi urban and rural areas. The Customer first applies for a home loan after that company validates the customer eligibility for loan. The Company wants to automate the loan eligibility process in real time based on customer details provided while filling out the online application form.

Notebook 1: mortgage-loans-model-building.ipynb

1. Data cleaning and exploration
2. Preprocessing the data
3. Visualizing the data
4. Exploring and optimizing various models
5. Evaluating the final model
6. Saving model components as pickle files

Notebook 2: mortgage-loans-predict-new-data.ipynb

1. Reading in the pickle files
2. Writing a function to preprocess the new data
3. Create artificial data & make predictions
4. Visualize the outputs

Final Application: app.py

1. Deployed on Heroku
2. Receives user inputs on all features
3. Makes predictions about loan status and probability
4. Allows user to set threshold for classification
5. Visualize the applicant in comparison to the training dataset

Data Sources

Source 1: Kaggle Source 2: Kaggle

Additional Examples:

• Linear regression model using the Ames housing dataset here.
• Logistic regression model using the home mortgage dataset here.
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