Goal One: To examine the data to identify any correlation using Pearson’s Correlation Coefficient (r). Goal Two: Create a forecasting machine learning model using past data from 1929-2017 to predict inflation and the appropriate federal funds rate. Since 1929, the U.S. has combated inflation. An inflation rate of 2% is believed to be an excellent environment for businesses and consumers. During deflation, corporations and local businesses lose pricing power. Businesses have to shed employees, future investments, and goods to maintain a profit which causes an economic slowdown during deflationary periods. During rising inflation above 2%, business profits rise temporally, but consumer pricing power is eroded over time, and it can lead to hyperinflation/economic crisis/economic slowdown. To prevent reoccurring economic collapses, deflation, and galloping inflation and to fix the lack of synergy with the other 12 regional banks, The U.S. founded the Federal Reserve (the central bank) on December 23, 1913. In this project, I will explore if correlations exist within the Monthly U.S. Consumer Price Index (CPI) average for all U.S. cities, Inflation Rate Year over Year (YoY), geopolitical events, economic events, GDP growth, Federal Funds Rate, and S&P 500 price annualized from 1929 to 2017. I will also examine if one of the Federal Reserve's most powerful tools, the Federal Funds Rate, is correlated with several factors listed above. I will create a forecasting algorithm using back-dated information to predict inflation and appropriate federal fund rates to combat inflation. To examine if the United States’ geopolitical, domestic, and economic events are correlated with the Inflation Rate YoY. I will also examine how the Federal Reserve Fund Rate affects the following: Monthly U.S. Consumer Price Index (CPI) average for all U.S. cities, Inflation Rate YoY, Geopolitical events, Economic Events, GDP Growth, and S&P 500 annualized prices utilizing the Pearson’s Correlation Coefficient (r). I will also create a forecasting machine learning model using back-dated information to predict inflation and appropriate federal funds rate

In this project, I am exploring a heart disease dataset for a university hospital. The results from my analysis will be used to develop predictive models that assess an individual’s risk of heart disease. I will use decision trees and logistical regressional models.

MAT 303 MODULE 2-4.