课程: Machine Learning with Data Reduction in Excel, R, and Power BI

今天就学习课程吧！

今天就开通帐号，24,600 门业界名师课程任您挑！

Solution: PCA

Solution: PCA

课程: Machine Learning with Data Reduction in Excel, R, and Power BI

开始一个月试用 购买团队方案

Solution: PCA

“

(upbeat music) - I'm going to create a new data frame called Denver for the daily temperature measurements from year 2010 and later. I'll filter our DF data frame variable so that the city equals Denver, and then I'll add a second condition by closing the parentheses around the first and adding an ampersand, and let's say DF year is greater than or equal to 2010. For the columns, I'm only going to select the date, the city, the TMIN column, and the TMAX column. Let's see what the original data points look by plotting the minimum temperature, which we'll put on the x-axis. Let's put the max on the y-axis, so it's going to go first. We see here that there's pretty strong correlation between the TMAX and the TMIN fields for Denver over the last ten years or so of weather data. What we're interested in doing with the PCA model is seeing how we can turn this scatterplot to identify some of the outliers and…

内容