Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
Tidy Tuesday screencast: predicting horror movie ratings
This was publishes on October 22nd, 2019
Hint: What’s the source of the data; what does the row represent; how many observations?; what are the variables; and what do they mean?
David Robinson analyzed a dataset of horror movies as an example of exploratory data analysis and machine learning in R, performed without looking at the data in advance. This includes demonstrating the machine learning method of lasso regression to predict horror movie ratings based on cast, genre, and plot.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave continued to insert the data he was given into the codes to best identify the different horror movie ratings. He imported the data from Rstudio and explored many differnt types of data as well.
In this video, most of the things that Robinson was doing is the same as what we do in class. Robinson imports the given data into the codes to determine wether the correlation is weak or strong. He also uses scatter plots and bar graphs to view the results of his findings, like what we do in class.
In the end of the video, Robinson major finding from this data analysis os that the drama, comedy and mystery horror movies have the best reviews.
I thought that this ideo was very interesting for many reasons. Robinson used parts of Rstudio and methods of data analysis that I had not seen before. I liked that he used many different types of charts and genres of horror movies to really solidify his results. I feel that he used every possibe method to get the best and most accurate results.