Predicting Loan Eligibility for Dream Housing Finance company

This is a Hackathon problem @Analytics Vidhya where we were given a Noisy Datasets and we were to come up with our best accuracy using Machine Leaning model. Before my final submission I tried different ML model for my best Accuracy to be delivered.

Following are the ML model that I tried:

1) Decision Tree Classifier

2) Random Forest Classifier

3) K-Nearest-Neighbors

4) Support Vector Classifier

At last I end up with KNN(score : 77.8) at my best.

Rank : 3300 out of 61417

Problem Statement

Dream Housing Finance company deals in all kinds of home loans. They have presence across all urban, semi urban and rural areas. Customer first applies for home loan and after that company validates the customer eligibility for loan.

Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have provided a dataset to identify the customers segments that are eligible for loan amount so that they can specifically target these customers.

Data Dictionary

Train file: CSV containing the customers for whom loan eligibility is known as 'Loan_Status'

Test file: CSV containing the customer information for whom loan eligibility is to be predicted