amazon.com employee access prediction

The goal of my project was to build a machine learning model, using supervised learning on historical data provided by amazon, that predicts an employee’s access needs, such that manual access transactions are minimized as the employee’s attributes change over time. The model takes as input an employee’s role information and a resource code and outputs a prediction of whether or not access should be granted. This was a binary classification problem (predict approval or disapproval). The data consists of real historical data collected from 2010 & 2011. Employees are manually allowed or denied access to resources over time. You must create an algorithm capable of learning from this historical data to predict approval/denial for an unseen set of employees. dataset link: [login to view URL] Requirements: • I want prediction with high accuracy in Kaggle site. I want to be in top 50 in kaggle leaderboard • Use python code in machine learning to run this data set. • I want 4 best algorithms to execute with this dataset. can you please suggest me best algorithms for this problem. and I want neural networks also. I don't want random forest and linear regression algorithms. • I want documentation for this project as much as information you can provide with plagiarism. • The code should be executed on the Kaggle website.