- Your title can change over time.
Details for Milestone are available on Canvas (left sidebar, Course Project).
{Through our analysis within this data science project, we hope that we will gain a deeper understanding of the researched company, and propose refined opinions regarding the researched topic from the consolidation of data. We hope to retain the technical skill to conduct similar types of research in the future. We wanted to work on this dataset as analyzing a company’s sales performance encompasses all of our group members’ financial interests. Michael's research interest revolves around economic environmental effects on industry performance. Kobe’s research interests involve company financial reports and trend predictions, and Fumiya’s research interests are consumer behaviour and price sensitivity. Some questions our group wants to explore would be: -Can future sale predictions be accurately made using the market's fuel price, consumer price index, unemployment rates, and temperature? -Does the trend of Walmart’s sales correlate with national CPI? -What weeks are Walmart the most/least profitable, and do holidays significantly impact these sales? In what direction? We could imagine building a dashboard because this macro information would be helpful for Walmart’s daily decision-making. If its procurement department understands the correlation between consumer behaviour and gas prices, it can order an accurate number of products.}
{The data within this dataset was provided by the Walmart Corporation, and consolidated by data experts at Kaggle Inc. The information was all collected through web based research using sources found on Google. Our data shows the weekly sales reports for 45 Walmart store locations from different regions. It shows how the company’s historical sales, fuel prices, consumer price index, temperature, and unemployment rates are compared to predict future sales demand accurately. This dataset provides the historical data that covers sales from February 5th, 2010, to November 1st, 2012, but it gets updated from contributors on a frequent basis. The main purpose of this data set is to use the elements of fuel prices, temperature, and whether the day is a holiday to predict consumer shopping behaviours. Also, influencing the estimation with macro economic information such as consumer price index and unemployment rates, further strengthening and developing the sales predictions.}
- Micheal Fang: I am a third year management student that loves to play table tennis and perform cardistry on leisure time.
- Kobe Brunetti: Im a third year management student that enjoys woodworking, gardening, and golf!
- Fumiya Hayakawa: I am a forth year management student who likes baseball, skiing,and swimming.
{You should use this area to add a screenshot of an interesting plot, or of your dashboard}
{Add your stuff here}