Predicting how capable each applicant is of repaying a loan (Kaggle Challenge).

Introduction: Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders.

Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.

While Home Credit is currently using various statistical and machine learning methods to make these predictions, they're challenging Kagglers to help them unlock the full potential of their data. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.

https://www.kaggle.com/c/home-credit-default-risk

The objective of this competition is to use historical loan application data to predict whether or not an applicant will be able to repay a loan. This is a standard supervised classification task:

Supervised: The labels are included in the training data and the goal is to train a model to learn to predict the labels from the features.

Classification: The label is a binary variable, 0 (will repay loan on time), 1 (will have difficulty repaying loan)

1. Clone repository and install requirements (use Python3.5)

git clone https://github.com/rakshithvasudev/Home-Credit-Default-Risk.git

pip3 install -r requirements.txt

2. Navigate inside the "Home-Credit-Default-Risk" folder and Run

jupyter notebook

Navigate inside the "Model Building" folder and launch the notebook & to run cells do:

Shift + Enter

• Jupyter Notebook - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages.
• scikit-learn - Machine Learning in Python.
• Pandas - pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
• etc

This project is licensed under the MIT License - see the LICENSE.md file for details

• Awesome answers at stackoverflow.
• Kaggle