This is the second project in Udacity's Machine Learning Nanodegree which is based on Supervised Learning Techniques
Problem Statement:
A local school district has a goal to reach a 95% graduation rate by the end of the decade by identifying students who need intervention before they drop out of school.
As a software engineer contacted by the school district, your task is to model the factors that predict how likely a student is to pass their high school final exam, by constructing an intervention system that leverages supervised learning techniques.
The board of supervisors has asked that you find the most effective model that uses the least amount of computation costs to save on the budget. You will need to analyze the dataset on students' performance and develop a model that will predict the likelihood that a given student will pass, quantifying whether an intervention is necessary.