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:
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:
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- 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