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Machine Learning is a branch of artificial intelligence that lets computers learn from data and improve their performance on a task without being explicitly programmed for every scenario.

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Content-based filtering is data collected from the user to produce more content that the person would like better.

Collaborative filtering is filtering with multiple people's data instead of just your data.

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We rate 23 songs and give the machine our ratings to use, plus more data like what attributes for songs we like and then uses our ratings and attributes to give us predictions some more accurate than the other.

1.)Danceability

2.)Liveliness

3.)Tempo

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The way you choose the appropriate leve of complexity for your graph/model is You want the line that fits the best not the one that fits all the points on it the that is called an overfit.

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The one attribute model didn't predict what I would rate certain songs very well at all but that's because it doesn't have enough information to rate the songs how I did. The 2 attribute model rated the songs a little better but still not completely accurate to how I would rate the songs.

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The person with the most similar response to me was chloe we had almost the exact same ratings for most of the songs. This shows me that collabrative filtering will use other peoples rating compared to mine. i could change the model to be more accurate by using songs that would represent the music i like.

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The ethics of machine learning is a vital topic because Machine learning systems are increasingly influencing everyday decisions ranging from what news people see to whether they get a loan or a job. I think the public needs to know that the predictions from machine learning arent perfect and arent the most accurate predictions