Jean Paul Izabayo

Predict the likelihood of a client to open a term deposit account. Using Bank Marketing dataset from OpenML https://www.openml.org/d/1461. The dataset was collected using phone calls to see whether they are willing to open an open term account in a Portuguese bank. The task is to use data analysis and machine learning to classify whether a client will open a term deposit account or not.

In this project, we are going to predict if a borrower will pay off their loan on time or not using machine learning model. We will use the financial lending data from lending club. Lending Club is a marketplace for personal loans that matches borrowers who are seeking a loan with investors looking to lend money and make a return.

Forecast excess returns on a monthly basis. According to Welch and Goyal (2008), numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the historical average. They argue that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models. A copy of their paper [“A Comprehensive Look at The Empirical Performance of Equity Premium Prediction”

The core data structure in Spark is a resilient distributed data set (RDD). As the name suggests, an RDD is Spark's representation of a data set that's distributed across the RAM, or memory, of a cluster of many machines. An RDD object is essentially a collection of elements we can use to hold lists of tuples, dictionaries, lists, etc. Similar to a pandas DataFrame, we can load a data set into an RDD, and then run any of the methods accesible to that object.

Using Multi layer perceptron to perform speech recognition. Here we use neural networks to perform speech recognition, using activation function like leaky Relu, Batch normalization technique to reduce internal covariate shift in data and hence improve the performance of the model.