In this project, I am an analyst working for a car maker, and I have access to a dataset that can be used to study the fuel economy of cars. As a car maker, I am interested in identifying and understanding factors that contribute to better fuel economy. This dataset includes several key variables related to fuel economy. Using this data, I will develop models to predict fuel economy and uncover insights to improve vehicle efficiency.

IBM Data Scientists created a fictional data set exploring employee attrition. We will explore each category within the data set. We will also compare and contrast the former workers with the other employees. Eventually, we will categorize the former and current employees into three groups: Category Green: Retained, Category Yellow: At-Risk, and Category Red: Attrited. We will also create a trial data set to determine if we can retain “at-risk” employees based on the data provided.

In this project, I am an analyst studying historical data to model key outcomes. As a risk analyst for a credit card company, I examine customer characteristics to assess the likelihood of default, helping calculate credit risk. For the government, I analyze wage growth patterns and related economic factors to support policy decisions on the labor force and economic agenda.

Since the late 1970’s corporations have used machine learning recommendation systems to understand user selections, user trends and consumer demands. Machine learning algorithms in 2022 can now predict future interests, engagement, current taste, new product experimentation and more! I will use The University of Minnesota team lab (Grouplens) MovieLens 10m dataset for this project. Grouplens selected 72,000 users at random to rate at least 20 movies for a combined 10 million ratings (view Reference Section for more information). Goals: I will explore the Movielens 10M data set to conduct the following: I will clean the data, investigate any NAs and examine the outliers that may skew the data needed to achieve the RMSE goal of .86490. Create a series of visualizations and examine each chart to understand what steps we need to complete to reach our RMSE goal. Create a trial recommender model to understand the RMSE. Create a recommendation system based on the code from the reference section and utilize Loss Function (RMSE), User effects, and Regularization. Finalize the machine learning algorithm to achieve the RMSE goal.

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.

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

MAT 303 MODULE 2-4.