machineLearningProject
This is a school project that was put in place in order to validate a master's degree Machine Learning module.
Subject :
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.
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.
As data scientist we were asked to help home credit bank to unlock the full potential of their data. Doing so will ensure that their 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.
In order to do this, we used Kaggle's datasets :
Tasks:
You can view the hole work process on the project list of tasks or on Wrike agile dashboard.
Tools:
- Anaconda/ Python
- Jupyter Notebook
- Power Bi
- PowerPoint
- Wrike
- Github
- Teams
Execution :
- To execute Notebooks, you we need to install Anaconda or Python pwith a specific virtual Env
- To display dashboard, you will need to install Power Bi version 2016. (We could not generate a web link to the dashboard because we don't own professionnal email adresses)