After completing the courses in the [Microsoft Professional Program Data Science Curriculum](http://aka.ms/lexdsc), it’s time to put all that you have learned into practice. Before entering this competition, you should register for the [Data Science Professional Project](https://www.edx.org/course/data-science-professional-project-microsoft-dat102x) course on edX and review the detailed information there. This competition concerns loan data. When a customer applies for a loan, banks and other credit providers use statistical models to determine whether or not to grant the loan based on the likelihood of the loan being repaid. The factors involved in determining this likelihood are complex, and extensive statistical analysis and modelling are required to predict the outcome for each individual case. You must implement a model that predicts loan repayment or default based on the data provided. The dataset used in this competition consists of synthetic data that was generated specifically for use in this project. The data is designed to exhibit similar characteristics to genuine loan data. After the competition ends, your final score will be calculated. When this is done, you will be notified to return to the edX course and enter the submission ID for your highest scoring entry to determine your grade in the course.
I have worked for two types of mentioned experiment:- Version_1 >> by using pure GUI of ML Machine to clean missing values &NAS. Version_2 >>> by using an mixed of GUI and Execute R script to clean the missing values and NAS . I prefer to go with competition by Version_2