This is a Machine Learning Project which aims to predict if a subject will repay the laon which is carried out by appliying the data analysis steps.

1. Problem Statement: The first important step which is to analyse if a subject will repay his loan.
2. Planning and collecting dats: The data required was retreived from " https://www.kaggle.com/sanket5/loan-prediction-binary-class-problem/notebook "
3. Data Preprocessing: Cleaning, replacing, filtering, reshaping, transforming etc, were applied to make the data ready for stastical analysis.
4. Exploring Data: Exploratory analysis is done using descriptive statistics (i.e. mean, median, standard deviation, frequencies, percentages and visualisation etc.) which will show things or raise new questions about the data.
5. Modelling Data: Machine Learning algorithum such as K-Nearest Neighbors(KNN), (Gaussian) Naive Bayes (NB), Decision Tree (DT) is applied in this project and know the best performing algorithum.
6. Reporting: The reporting/interpretation of the results in this project is carried with detail throughout, after each code and output for better understanding of the analysis done.

The first 4 steps are done in phase 1 followed by 5 and 6 steps in phase 2 of the project and finally, a conclusion was made by analysing the best performing algorithum to predict the best possible outcome of whether a subject would repay back his loan or not.