Data cleaning looks good. Some of the "nationaldata" data frame column names could had an "" to match the others. Having two new sources is good.
I get that the model is used to predict the outcome of an election but I'm not really sure how to read the graph to get that. Why are there so many data points at the 0.0 and 1.0 parts of the Y axis? I'd expect a lot to be close to 1 or 0 but not on it.
Very good R proficiency. The blocks of code are separated nicely and look professional.
It's interesting that incumbency was not a good predictor. I think the general public would find this interesting and be able to follow for the most part.
Good critical thinking. Wanting to use this to figure out why we pick certain congresspeople can have a big impact on future elections if a conclusion from the data is reached and used to inform citizens.
Feedback: I think you could benefit from more visualizations. It seems like you have the data there that would make for some interesting graphs and charts.
Data cleaning looks good. Some of the "nationaldata" data frame column names could had an "" to match the others. Having two new sources is good.
I get that the model is used to predict the outcome of an election but I'm not really sure how to read the graph to get that. Why are there so many data points at the 0.0 and 1.0 parts of the Y axis? I'd expect a lot to be close to 1 or 0 but not on it.
Very good R proficiency. The blocks of code are separated nicely and look professional.
It's interesting that incumbency was not a good predictor. I think the general public would find this interesting and be able to follow for the most part.
Good critical thinking. Wanting to use this to figure out why we pick certain congresspeople can have a big impact on future elections if a conclusion from the data is reached and used to inform citizens.
Feedback: I think you could benefit from more visualizations. It seems like you have the data there that would make for some interesting graphs and charts.