This is an end-to-end ML project, which aims at developing a classification model for the problem of classifying a customer profile into 2 types based on the risk factor, using a given labeled dataset.
Multiple classifiers were tested and the final fclassifier used for this project is XGBoost tClassifier.
Deployed in Heroku.
Link to the application : https://south-german-credit-score.herokuapp.com/
Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that you can use to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification.
The goal of this project, you have to build a model to predict whether the person, described by the attributes of the dataset, is a good (1) or a bad (0) credit risk
This dataset is taken from the UCI Machine Learning Repository. It contains information on defaults, demographic factors, credit data etc. of customers.
Link : https://archive.ics.uci.edu/ml/datasets/South+German+Credit
The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.
