The dataset contains the following information for each track in Jacob's Optimum Music Feast playlist:

1. Spotify ID: The unique identifier for the track on Spotify.
2. Artist IDs: The unique identifiers for the artist(s) of the track on Spotify.
3. Track Name: The name of the track.
4. Album Name: The name of the album where the track is from.
5. Artist Name(s): The name of the artist(s) of the track.
6. Release Date: The date when the track was released.
7. Duration (ms): The duration of the track in milliseconds.
8. Popularity: The popularity of the track on Spotify, as a measure from 0 to 100.
9. Added By: The Spotify user who added the track to the playlist.
10. Added At: The date and time when the track was added to the playlist.
11. Danceability: A measure from 0.0 to 1.0 describing the suitability of a track for dancing.
12. Energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity.
13. Key: The key the track is in.
14. Loudness: The overall loudness of a track in decibels (dB).
15. Mode: The modality (major or minor) of a track, the type of scale from which its melodic content is derived.
16. Speechiness: Speechiness detects the presence of spoken words in a track.
17. Acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic.
18. Instrumentalness: Predicts whether a track contains no vocals.
19. Liveness: Detects the presence of an audience in the recording.
20. Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.
21. Tempo: The overall estimated tempo of a track in beats per minute (BPM).
22. Time Signature: An estimated overall time signature of a track.

To develop a scoring model, we need to find the factors that predict whether a song will be added to Jacob's playlist. A sensible approach might be to use a machine learning algorithm such as logistic regression or a decision tree, where the target variable would be whether the song is in the playlist, and the features would be the various characteristics of the songs (e.g., danceability, energy, key, loudness, etc.). However, please note that the model's performance would be largely dependent on the dataset's diversity and size.

Before we can proceed with model development, let's explore the dataset a bit more. Let's look at the distribution of the numerical features, the number of unique artists, and the time range of the songs added to the playlist.

The histograms show the distribution of the various numerical features in the dataset. Some observations include:

• The duration of songs varies widely, with many songs in the 200,000 to 300,000 ms range.
• Popularity is slightly skewed towards lower values, indicating that there are quite a few songs that are not as popular on Spotify.
• Danceability, Energy, and Valence have somewhat uniform distributions, indicating that Jacob's playlist contains a variety of songs, from less danceable to highly danceable, from low to high energy, and from sad to happy.
• Loudness is approximately normally distributed around -10 dB.
• Most songs are in a major key (Mode=1).
• Most songs have low Speechiness, indicating that they contain more music than spoken words.
• Acousticness and Instrumentalness are quite varied, indicating a mix of acoustic, non-acoustic, vocal, and instrumental tracks.
• Most songs have low Liveness, indicating that they are studio recordings rather than live performances.
• Tempo varies widely, with a slight peak around 120-130 BPM, a common tempo for many genres of music.
• Most songs are in 4/4 time (Time Signature=4), which is the most common time signature in Western music.

The playlist contains songs from 717 unique artists, showcasing a wide variety of musical influences. The songs were added to the playlist from June 16, 2016 to July 5, 2023, a span of about 7 years.

Now, let's proceed to create a scoring model. To do so, we'll need to define our target variable. However, all the songs in this dataset are from Jacob's playlist, so they are all "positive" examples. To create a predictive model, we would need a balanced dataset containing both songs that Jacob would add to his playlist (positive examples) and songs he wouldn't (negative examples). Without this, we cannot train a model to differentiate between the two.

If we had such a dataset, we could use the features in this dataset to train a binary classification model, such as logistic regression or a decision tree, with the target variable being whether or not a song is in Jacob's playlist. Then, we could assign a score to a song based on the predicted probability from the model: a song with a high predicted probability of being in the playlist would get a high score, and vice versa.

Unfortunately, without a set of negative examples, we can't proceed with this approach. Do you have access to additional data, such as a set of songs not in Jacob's playlist?

To score how "surprising" a track would be to add to the playlist based on the existing data, we can create a model that uses the statistical properties of the numerical features in the existing playlist. For each numerical feature, we can calculate its mean and standard deviation in the existing playlist, and then calculate how many standard deviations away from the mean the feature value of the new track is. The more standard deviations away, the more "surprising" it would be for the track to be added to the playlist.

We can then sum up these "surprise" scores for all features to get a total "surprise" score for the track. We'll normalize this score to be between 1 and 100, with 1 being the least surprising (most predictable) and 100 being the most surprising (least predictable).

Let's pick a random track from the playlist and calculate its "surprise" score.

The randomly selected track is "mostly chimes" by Adrianne Lenker from the album "instrumentals". The calculated "surprise" score for this track is approximately 508.45.

Please note that this score doesn't necessarily reflect how "surprising" the track would be to someone familiar with Jacob's musical preferences, as it's based solely on the statistical properties of the numerical features in the existing playlist. The score doesn't take into account the specific musical qualities of the track or Jacob's subjective tastes.

Also, bear in mind that the range of this score is not from 1 to 100, as originally requested. It can potentially be much higher, depending on how many standard deviations away from the mean the track's features are. If you want the score to be between 1 and 100, we would need to use a different method to calculate it.

The top 10 tracks with the highest "surprise" scores are:

1. "Music for 18 Musicians" by Steve Reich, Ida Szabo, Ágnes Dobszay, Zsuzsanna Aba-Nagy, and others from the album "Reich: Music for 18 Musicians" - Score: 37.48
2. "mostly chimes" by Adrianne Lenker from the album "instrumentals" - Score: 25.63
3. "Black Swan (In Memory Of Susan Portlynn Romeo)" by Wayne Shorter from the album "High Life" - Score: 25.55
4. "The Alphabet Song/Twinkle, Twinkle, Little Star" by Bobby McFerrin from the album "On The Beach (Live 1984)" - Score: 22.89
5. "Getonup" by Knxwledge from the album "Anthology" - Score: 21.77
6. "Polaris Voyage for Orchestra" by Thomas Adès, London Symphony Orchestra from the album "Adès: Asyla, Tevot, Polaris" - Score: 21.56
7. "Recomposed By Max Richter: Vivaldi, The Four Seasons, 1. Spring 1" by Max Richter, Konzerthaus Kammerorchester Berlin, Andre de Ridder from the album "Recomposed By Max Richter: Vivaldi, The Four Seasons" - Score: 21.29
8. "Dreams of Stairs" by Taylor Deupree from the album "Faint" - Score: 21.03
9. "Dreams of Stairs" by Taylor Deupree from the album "Faint" - Score: 21.03
10. "Moon River(Original Main Title)" by Henry Mancini from the album "Breakfast At Tiffany's (50th Anniversary Edition)" - Score: 21.01

The bottom 10 tracks with the lowest "surprise" scores are:

1. "Creepin'" by Stevie Wonder from the album "Fulfillingness' First Finale" - Score: 5.76
2. "Weightless" by Becca Stevens from the album "Weightless" - Score: 5.74
3. "Weightless" by Becca Stevens from the album "Weightless" - Score: 5.74
4. "Pretty Polly" by Sam Amidon from the album "Sam Amidon" - Score: 5.59
5. "Poetry and Aeroplanes" by Teitur from the album "Poetry & Airplanes" - Score: 5.58
6. "My Possession" by Dirty Projectors from the album "5EPs" - Score: 5.42
7. "Every Little Thing She Does Is Magic" by Jacob Collier, Metropole Orkest, Jules Buckley from the album "Djesse Vol. 1" - Score: 5.30
8. "She's Got a Problem" by Fountains Of Wayne from the album "Fountains of Wayne" - Score: 5.27
9. "Holy Ghost" by Kim Burrell from the album "Everlasting Life" - Score: 5.26
10. "The Ride" by Joan As Police Woman from the album "Real Life" - Score: 4.28

Remember that these "surprise" scores are based on the statistical properties of the numerical features in the playlist, and they do not necessarily reflect how "surprising" a track would be to someone familiar with Jacob's musical preferences.

Here are some notable insights about the Time Signature field:

Count of songs:

• The majority of songs (78%) in the playlist have a time signature of 4, which is the most common time signature in Western music.
• About 16.55% of the songs have a time signature of 3, which is often used in waltzes and other types of music with a "triplet" feel.
• Only about 3.14% of the songs have a time signature of 5, which is less common and often associated with more complex or experimental types of music.

Average "surprise" score:

• Songs with a time signature of 4 have the lowest average "surprise" score (about 10.33), suggesting that they are less "surprising" or more predictable based on the existing playlist.
• Songs with a time signature of 3 have a slightly higher average "surprise" score (about 12.55), while songs with a time signature of 5 have an even higher average "surprise" score (about 12.63).
• The song with a time signature of 0 (which might indicate an absence or ambiguity of a clear time signature) has the highest average "surprise" score (about 25.55).

Songs with the highest and lowest "surprise" scores:

• For a time signature of 3, the song with the highest "surprise" score is "Music for 18 Musicians" by Steve Reich, Ida Szabo, Ágnes Dobszay, Zsuzsanna Aba-Nagy, and others, and the song with the lowest "surprise" score is "Just From Chevron" by Dirty Projectors.
• For a time signature of 4, the song with the highest "surprise" score is "Polaris Voyage for Orchestra" by Thomas Adès, London Symphony Orchestra, and the song with the lowest "surprise" score is "The Ride" by Joan As Police Woman.
• For a time signature of 5, the song with the highest "surprise" score is "Getonup" by Knxwledge, and the song with the lowest "surprise" score is "Free Mind" by Tems.

Remember, these "surprise" scores are based on how many standard deviations away from the mean the song's features are, and they do not necessarily reflect how "surprising" a song would be to someone familiar with Jacob's musical preferences.

The most interesting insights for a die-hard fan of Jacob Collier's playlist might revolve around understanding the musical preferences and influences of Jacob as reflected in his playlist. Here are some potential highlights:

1. Diversity of Time Signatures: The playlist contains songs with a wide range of time signatures, from the most common 4/4 time to less common signatures like 3/4 and 5/4. This shows Jacob's appreciation for a variety of rhythmic structures and might suggest an interest in rhythmic complexity and experimentation.
2. Unexpected "Surprise" Scores: The "surprise" scores can provide insight into which tracks might be considered more unique or unexpected additions to the playlist based on their musical characteristics. For instance, "Music for 18 Musicians" by Steve Reich and others, which has a time signature of 3, has the highest "surprise" score among all tracks. This could be an interesting point for fans who want to explore the more unusual or unexpected tracks on the playlist.
3. Highly represented Artists: The diversity of artists in the playlist, with 717 unique artists, shows that Jacob's musical influences and tastes are wide-ranging. However, fans could delve deeper to see if there are any artists that appear particularly frequently in the playlist, which could suggest major influences on Jacob's own music.
4. Variety of Musical Features: The distributions of various musical features like danceability, energy, loudness, and acousticness show that the playlist spans a wide range of musical styles and moods. This could provide fans with a deeper understanding of the types of music that Jacob enjoys and that might influence his own compositions.
5. Historical Perspective: The dates when the songs were added to the playlist span a period of about 7 years, from 2016 to 2023. Fans could analyze how the musical features of the songs added to the playlist have changed over time, which could provide insight into how Jacob's musical tastes and influences have evolved.

Remember, these insights are based on the analysis of the numerical features in the playlist and the "surprise" scores calculated from these features. They do not necessarily reflect Jacob's subjective musical tastes or the specific reasons why he chose to add each track to the playlist.

Here are some interesting time-series trends over the 7-year period:

Number of Songs Added Each Year:

• The number of songs added to the playlist has generally increased over time, with a peak in 2020 when 335 songs were added. The number of songs added in 2021 and 2022 decreased slightly, and 129 songs were added in 2023 up until the date of the data.

Average Musical Features Over Time:

• The average popularity of the songs added to the playlist has generally increased over time, from about 13.65 in 2016 to about 33.20 in 2023. This might suggest that Jacob has been adding more popular songs to his playlist in recent years.
• The average danceability, energy, and valence of the songs have remained relatively stable over time, indicating consistent preferences in these aspects of the music.
• The average loudness of the songs has increased from about -13 dB in 2016 to about -11 dB in 2023, suggesting a trend towards louder music.
• The average speechiness of the songs has increased slightly over time, from about 0.07 in 2016 to about 0.08 in 2023. This might indicate a slight preference for music with more spoken words in recent years.
• The average acousticness of the songs has remained relatively stable over time, suggesting consistent preferences in terms of the acoustic quality of the music.
• The average instrumentalness of the songs has decreased slightly over time, from about 0.33 in 2016 to about 0.23 in 2023. This might indicate a slight preference for music with vocals in recent years.

Remember, these trends are based on averages and do not necessarily reflect the variety of music in the playlist each year. Also, the data for 2023 is incomplete, as it only includes songs added up until the date of the data.

The Key feature, which represents the key the track is in, has the following correlations with the other numerical features:

• Mode (-0.108): This is a negative correlation, indicating that tracks in a major key (Mode=1) tend to have lower key values. This could be due to the distribution of major and minor keys in the dataset.
• Instrumentalness (-0.069): There's a slight negative correlation, suggesting that instrumental tracks might be slightly more likely to be in a lower key, though the correlation is quite weak.
• Acousticness (-0.046), Duration (ms) (-0.033), Tempo (-0.009), Danceability (0.007), Popularity (0.009), Time Signature (0.014), Liveness (0.029), Speechiness (0.030), and Valence (0.037): All of these features have very weak correlations with Key, suggesting that there's no strong relationship between the key of a track and these features.
• Loudness (0.057) and Energy (0.090): These features have a slight positive correlation with Key, suggesting that tracks in a higher key might be slightly louder and more energetic, though again the correlations are quite weak.

It's worth noting that these are only correlation values, and correlation does not imply causation. Also, music is a complex combination of many features, and the key of a track is just one aspect that contributes to its overall character. The relationships between key and other features can be influenced by many other factors, such as the genre of the music, the instrumentation, and the artist's personal style and preferences.

Here are some notable correlations with each unique key value:

• Key 0: Tracks with key 0 have a relatively high average acousticness (0.52), suggesting they are more likely to be acoustic. They also have a relatively high mode value (0.78), indicating they are more likely to be in a major key.
• Key 1: Tracks with key 1 have a relatively high average speechiness (0.11), suggesting they might have more spoken words. They also have a higher average energy (0.53) and danceability (0.58) compared to other keys.
• Key 2: Tracks with key 2 have the highest mode value (0.90), indicating they are most likely to be in a major key. They also have a relatively high average acousticness (0.53).
• Key 3: Tracks with key 3 have the highest average acousticness (0.69), suggesting they are the most likely to be acoustic. They also have a relatively low average energy (0.34) and danceability (0.48).
• Key 4: Tracks with key 4 have a relatively low mode value (0.64), indicating they are less likely to be in a major key. They also have a relatively high average loudness (-12.15 dB).
• Key 5: Tracks with key 5 have a relatively low mode value (0.59), suggesting they are less likely to be in a major key. They also have a relatively high average danceability (0.54) and energy (0.47).
• Key 6: Tracks with key 6 have the lowest mode value (0.48), indicating they are the least likely to be in a major key. They also have a relatively high average energy (0.51).
• Key 7: Tracks with key 7 have a relatively high mode value (0.84), indicating they are more likely to be in a major key. They also have a relatively high average acousticness (0.53).
• Key 8: Tracks with key 8 have the highest average speechiness (0.09), suggesting they might have more spoken words. They also have a relatively high mode value (0.85).
• Key 9: Tracks with key 9 have a relatively high mode value (0.82), indicating they are more likely to be in a major key. They also have a relatively high average danceability (0.56).
• Key 10: Tracks with key 10 have the highest average speechiness (0.11), suggesting they might have more spoken words. They also have the highest average valence (0.55), indicating they are the most likely to convey a positive mood.
• Key 11: Tracks with key 11 have a relatively low mode value (0.54), indicating they are less likely to be in a major key. They also have the highest average energy (0.58) and the lowest average acousticness (0.37).

Again, it's important to remember that these are average values and may not represent all tracks with a given key value. The musical characteristics of a track can be influenced by many other factors, such as the genre, instrumentation, and artist's personal style and preferences.

Here's a summary of the notable correlations with each key:

This is a general analysis based on the average values of the features in the dataset and may not represent all tracks with a given key value. The musical characteristics of a track can be influenced by many other factors, such as the genre, instrumentation, and artist's personal style and preferences.