R codes and analysis output for Day 3

R codes and analysis output for Day 2

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.

R codes and analysis output for Day 1

Data Source: https://creativecommons.org/licenses/by-nc-sa/4.0/ Imported CSV into google sheets and ran VLOOKUP to calculate information such as profit & revenue by category.

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.

As usual, in this weekly review of December 29th, 2024, we will examine the top-performing and underperforming stocks on the Euronext and BRVM (Bourse Régionale des Valeurs Mobilières) financial markets.