Kiva is a nonprofit organization founded in October 2005. It uses crowd-financing to lend money via Internet to low-income entrepreneurs and students over 80 countries all over the world. The main goal of this project was to estimate a Kiva borrower’s level of welfare using relevant information. The relevant information and the data sets were collected from Kaggle (an online community of data scientists and machine learning practitioners and an open source of original data sets) and the kiva Data Snapshots. This data sets were used to predict any delay and the default loans using Logistic regression in R programming language, then predicted the model accuracy using confusion matrix. Secondly, the prospective regions and the fields of lending money from the lenders point of view was also retrieve from the analysis.