This repository contains my exercise from the course Loan Default Prediction using Machine Learning on ALX BIDA, this course is written in Chapter and this repository is being updated per chapter done Chapter 1 summary

Basic use case for manipulating data for the ML Project

Identified target variable

Dealt with missing data

Calculated ages & extracted data from columns Chapter 2 Summary In this chapter, we have demonstrated some techniques to carry out basic exploratory analysis

Specific techniques used for exploration may be dependent on both the data and its context

Spend some time now exploring the data further

Combine these techniques with your own intuition to formulate some hypothesis as to why a particular person might default on their loan Chapter 3 Summary In this chapter, we have performed some basic feature engineering

We have created a new categorical column using binning on 'DISBURSED_AMOUNT'

We have combined features to create new ones

We have performed MinMax scaling on our numeric variables