MORTGAGE LOAN AQUISITION REQUIREMENT

This entire project encompasses both Data Analysis and Machine Learning. It was carefully structured and compiled for easy understanding.

Installation:

To run this notebook you can either install.

• Download anaconda from anaconda site this have almost all dependencies pre-installed. Feel free to use any environment of choice

Dependencies:

• NumPy
• IPython
• Pandas
• SciKit-Learn
• Matplotlib

Personal project | Mortgage loan elegibility prediction

The Home Mortgage Disclosure Act (HMDA) requires many financial institutions to maintain, report, and publicly disclose information about mortgages. These public data are important because:

• • they help show whether lenders are serving the housing needs of their communities.
  • help authourities to determine and fish out all predatory act of lending.
  • they give public officials information that helps them make decisions and policies.
  • They shed light on lending patterns that could be discriminatory. Eg. a reported increase in mortgage borrowing by blacks and Hispanics as of 1993.

On my Kaggle site My Homepage.

Goal for this Notebook:

Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed for those looking to get into the field Data Science or those who are already in the field and looking to solve a real world project with python.

This Notebook will teach the following:

Data Handling

• Importing Data with Pandas
• Cleaning Data
• Exploring Data through Visualizations with Matplotlib
• Doing predictive Analysis with various Machine Learning Algorithms

Data Analysis/Machine Learning

• Supervised Machine learning Techniques: + RandomForestClassifier + StratifiedKfold ( 5 folds) + ETC

Valuation of the Analysis

• K-folds cross validation to valuate results locally
• Output the results from the IPython Notebook to Kaggle

Results obtained

• Was able to derive excerpt insights to give pro recommendation to borrowers
• Was able to predict applicant loan approval with 74% accuracy