This project aims to predict IMDB scores of different movies based on some features like budget, runtime, popularity, etc. by using a data set that contains 3600 observations and 22 features. So far they only used numeric features to construct a linear regression.
There are several things I like about this report:
The topic is interesting and meaningful.
The report is clearly divided into several parts, which is easy to read.
However, there are some aspects I hope they can consider improving on.
- The first impression was that the results shown in the report were really preliminary. As they proposed, there would be much work left for the next month.
- No details were provided about data cleaning except for dealing with missing data.
- To answer the question "how effective the model is?", they only relied on seeing the significance levels and R^2, which is not very persuasive.
This project aims to predict IMDB scores of different movies based on some features like budget, runtime, popularity, etc. by using a data set that contains 3600 observations and 22 features. So far they only used numeric features to construct a linear regression.
There are several things I like about this report:
The topic is interesting and meaningful.
The report is clearly divided into several parts, which is easy to read.
However, there are some aspects I hope they can consider improving on.