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..
Analysing the transcation details and their Credict history using Matplotlib, Seabor and plotly.
Kiva.org is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world.
Kiva lenders have provided over $1 billion dollars in loans to over 2 million people.
For the locations in which Kiva has active loans, your objective is to pair Kiva's data with additional data sources to estimate the welfare level of borrowers in specific regions, based on shared economic and demographic characteristics.
The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
Analysing the data and drawing the relations to the causes of Diabeties.