Lyrical Success – Preparing the Data

The data created in the previous step needs a little bit of cleaning up before we can get into the model building. It is a common step that needs to be undertaken to ensure that the data can be loaded into models without any issues.

Part 3 of 4

Steps for Preparing the Data

Clean the Rankings
Match the Song Ranks

Step 1 – Clean the Rankings

The first step is to clean up the rankings files that I created in the previous step.

Step 1.01 Load the Packages

Reference the necessary packages. Proto, gsubfn, magrittr, dplyr, and lubridate.

# ----------------------------------------------------------------------------
# clean up the ranking files and merge with the lyrics files
# ----------------------------------------------------------------------------
library(proto ,quietly = TRUE, warn.conflicts = FALSE) #used by gsubfn
library(gsubfn ,quietly = TRUE, warn.conflicts = FALSE)

library(magrittr ,quietly = TRUE, warn.conflicts = FALSE)
library(dplyr ,quietly = TRUE, warn.conflicts = FALSE)

library(lubridate ,quietly = TRUE, warn.conflicts = FALSE)

# ----------------------------------------------------------------------------

# clean up the ranking files and merge with the lyrics files

# ----------------------------------------------------------------------------

library(proto ,quietly = TRUE, warn.conflicts = FALSE) #used by gsubfn

library(gsubfn ,quietly = TRUE, warn.conflicts = FALSE)

library(magrittr ,quietly = TRUE, warn.conflicts = FALSE)

library(dplyr ,quietly = TRUE, warn.conflicts = FALSE)

library(lubridate ,quietly = TRUE, warn.conflicts = FALSE)

Step 1.02 Load the Ranking Data into Data Frames

Will use the standard library to read the flat files, and then store the data into a data frame. All of the flat files have the same number of columns and the column order is identical for each file, no need to reorganize or make adjustments afterwards.

Additionally because I created the ranking files in the prior step, I know that there are no null values.

# load the ranking data
df_rankings_drake <- read.delim(paste0(getwd(), "/ranks/Drake.txt")
                                    , header = TRUE
                                    , stringsAsFactors = FALSE
                             )

df_rankings_onedirection <- read.delim(paste0(getwd(), "/ranks/One-Direction.txt")
                                      , header = TRUE
                                      , stringsAsFactors = FALSE
                            )

df_rankings_pink <- read.delim(paste0(getwd(), "/ranks/Pink.txt")
                              , header = TRUE
                              , stringsAsFactors = FALSE
                            )

df_rankings_rihanna <- read.delim(paste0(getwd(), "/ranks/Rihanna.txt")
                               , header = TRUE
                               , stringsAsFactors = FALSE
                              )

df_rankings_taylorswift <- read.delim(paste0(getwd(), "/ranks/Taylor-Swift.txt")
                                    , header = TRUE
                                    , stringsAsFactors = FALSE
                                    )

df_rankings_u2 <- read.delim(paste0(getwd(), "/ranks/U2.txt")
                            , header = TRUE
                            , stringsAsFactors = FALSE
                            )

# load the ranking data

df_rankings_drake <- read.delim(paste0(getwd(), "/ranks/Drake.txt")

, header = TRUE

, stringsAsFactors = FALSE

)

df_rankings_onedirection <- read.delim(paste0(getwd(), "/ranks/One-Direction.txt")

, header = TRUE

, stringsAsFactors = FALSE

)

df_rankings_pink <- read.delim(paste0(getwd(), "/ranks/Pink.txt")

, header = TRUE

, stringsAsFactors = FALSE

)

df_rankings_rihanna <- read.delim(paste0(getwd(), "/ranks/Rihanna.txt")

, header = TRUE

, stringsAsFactors = FALSE

)

df_rankings_taylorswift <- read.delim(paste0(getwd(), "/ranks/Taylor-Swift.txt")

, header = TRUE

, stringsAsFactors = FALSE

)

df_rankings_u2 <- read.delim(paste0(getwd(), "/ranks/U2.txt")

, header = TRUE

, stringsAsFactors = FALSE

)

Step 1.03 Function to Replace White Space

The function will replace the line feeds, tabs or any white space in the peek_rank column.

# ----------------------------------------------------------------------------
# update the data frames
# ----------------------------------------------------------------------------
replace_empty <- function(textcontent){
# remove line feeds, tabs, and white space
  if("factor" %in% class(textcontent)){
    textcontent <- as.character(textcontent)
  }
  replacement_list <- list("\n" = " ", "\t" = "	")
  textcontent <- ifelse(textcontent=="", "#1 on" , textcontent)
  textcontent <- gsubfn("." , replacement_list,textcontent)
  
  return(textcontent)
}

# ----------------------------------------------------------------------------

# update the data frames

# ----------------------------------------------------------------------------

replace_empty <- function(textcontent){

# remove line feeds, tabs, and white space

if("factor" %in% class(textcontent)){

textcontent <- as.character(textcontent)

}

replacement_list <- list("\n" = " ", "\t" = " ")

textcontent <- ifelse(textcontent=="", "#1 on" , textcontent)

textcontent <- gsubfn("." , replacement_list,textcontent)

return(textcontent)

}

Step 1.04 Replace the Data Frame

For each artist, take the data frame will have four columns.

artist: is the name of the artist
song_title: name of the song
peek_rank: the highest position the song reached on the Billboard Hot 100 rankings
peek_date: the date the song reached its highest rank on the Billboard Hot 100 rankings

# -- Drake
df_rankings_drake <- df_rankings_drake %>%
                      transmute(artist = "Drake"
                                ,song_title = song_title
                                ,peek_rank = replace_empty(peek_rank)
                                ,peek_date = mdy(peek_date)
                                )

# -- one direction
df_rankings_onedirection <- df_rankings_onedirection %>%
                            transmute(artist = "OneDirection"
                                      ,song_title = song_title
                                      ,peek_rank = replace_empty(peek_rank)
                                      ,peek_date = mdy(peek_date)
                                      )

# -- Pink
df_rankings_pink <- df_rankings_pink %>%
                    transmute(artist = "Pink"
                              ,song_title = song_title
                              ,peek_rank = replace_empty(peek_rank)
                              ,peek_date = mdy(peek_date)
                    )

# -- Rihanna
df_rankings_rihanna <- df_rankings_rihanna %>%
                      transmute(artist = "Rihanna"
                                ,song_title = song_title
                                ,peek_rank = replace_empty(peek_rank)
                                ,peek_date = mdy(peek_date)
                      )

# -- Taylor Swift
df_rankings_taylorswift <- df_rankings_taylorswift %>%
                            transmute(artist = "TaylorSwift"
                                      ,song_title = song_title
                                      ,peek_rank = replace_empty(peek_rank)
                                      ,peek_date = mdy(peek_date)
                            )


# -- U2
df_rankings_u2 <- df_rankings_u2 %>%
                  transmute(artist = "U2"
                            ,song_title = song_title
                            ,peek_rank = replace_empty(peek_rank)
                            ,peek_date = mdy(peek_date)
                  )

# -- Drake

df_rankings_drake <- df_rankings_drake %>%

transmute(artist = "Drake"

,song_title = song_title

,peek_rank = replace_empty(peek_rank)

,peek_date = mdy(peek_date)

)

# -- one direction

df_rankings_onedirection <- df_rankings_onedirection %>%

transmute(artist = "OneDirection"

,song_title = song_title

,peek_rank = replace_empty(peek_rank)

,peek_date = mdy(peek_date)

)

# -- Pink

df_rankings_pink <- df_rankings_pink %>%

transmute(artist = "Pink"

,song_title = song_title

,peek_rank = replace_empty(peek_rank)

,peek_date = mdy(peek_date)

)

# -- Rihanna

df_rankings_rihanna <- df_rankings_rihanna %>%

transmute(artist = "Rihanna"

,song_title = song_title

,peek_rank = replace_empty(peek_rank)

,peek_date = mdy(peek_date)

)

# -- Taylor Swift

df_rankings_taylorswift <- df_rankings_taylorswift %>%

transmute(artist = "TaylorSwift"

,song_title = song_title

,peek_rank = replace_empty(peek_rank)

,peek_date = mdy(peek_date)

)

# -- U2

df_rankings_u2 <- df_rankings_u2 %>%

transmute(artist = "U2"

,song_title = song_title

,peek_rank = replace_empty(peek_rank)

,peek_date = mdy(peek_date)

)

Step 2 – Match the Song Ranks

Now that the rankings files have been loaded and stored into data frames, the next step is to join the ranking files with the lyric files.

Step 2.01 Load the necessary packages

Load the necessary packages magrittr, dplyr, and readr.

# ----------------------------------------------------------------------------
# take the ranking files and merge with the song lists
# ----------------------------------------------------------------------------
library(magrittr ,quietly = TRUE, warn.conflicts = FALSE)
library(dplyr ,quietly = TRUE, warn.conflicts = FALSE)
library(readr ,quietly = TRUE, warn.conflicts = FALSE)

# ----------------------------------------------------------------------------

# take the ranking files and merge with the song lists

# ----------------------------------------------------------------------------

library(magrittr ,quietly = TRUE, warn.conflicts = FALSE)

library(dplyr ,quietly = TRUE, warn.conflicts = FALSE)

library(readr ,quietly = TRUE, warn.conflicts = FALSE)

Step 2.02 Load the Lyrics into Data Frames

Load each of the lyric .csv files that I created from the previous step into data frames.

# load the songs lists
df_songs_drake <- read.csv(paste0(getwd(), "/lyrics/album_song/Drake_lyrics.csv")
                                , header = TRUE
                                , stringsAsFactors = FALSE
)

df_songs_onedirection <- read.csv(paste0(getwd(), "/lyrics/album_song/One-Direction_lyrics.csv")
                                       , header = TRUE
                                       , stringsAsFactors = FALSE
)

df_songs_pink <- read.csv(paste0(getwd(), "/lyrics/album_song/pink_lyrics.csv")
                               , header = TRUE
                               , stringsAsFactors = FALSE
)

df_songs_rihanna <- read.csv(paste0(getwd(), "/lyrics/album_song/Rihanna_lyrics.csv")
                                  , header = TRUE
                                  , stringsAsFactors = FALSE
)

df_songs_taylorswift <- read.csv(paste0(getwd(), "/lyrics/album_song/Taylor-Swift_lyrics.csv")
                                      , header = TRUE
                                      , stringsAsFactors = FALSE
)

df_songs_u2 <- read.csv(paste0(getwd(), "/lyrics/album_song/U2_lyrics.csv")
                             , header = TRUE
                             , stringsAsFactors = FALSE
)

# load the songs lists

df_songs_drake <- read.csv(paste0(getwd(), "/lyrics/album_song/Drake_lyrics.csv")

, header = TRUE

, stringsAsFactors = FALSE

)

df_songs_onedirection <- read.csv(paste0(getwd(), "/lyrics/album_song/One-Direction_lyrics.csv")

, header = TRUE

, stringsAsFactors = FALSE

)

df_songs_pink <- read.csv(paste0(getwd(), "/lyrics/album_song/pink_lyrics.csv")

, header = TRUE

, stringsAsFactors = FALSE

)

df_songs_rihanna <- read.csv(paste0(getwd(), "/lyrics/album_song/Rihanna_lyrics.csv")

, header = TRUE

, stringsAsFactors = FALSE

)

df_songs_taylorswift <- read.csv(paste0(getwd(), "/lyrics/album_song/Taylor-Swift_lyrics.csv")

, header = TRUE

, stringsAsFactors = FALSE

)

df_songs_u2 <- read.csv(paste0(getwd(), "/lyrics/album_song/U2_lyrics.csv")

, header = TRUE

, stringsAsFactors = FALSE

)

Step 2.03 Update the Data Frames Dropping the song_url Column

I will convert the data frame into a tibble, selecting all columns except the song_url column

# ----------------------------------------------------------------------------
# update the data frames
# ----------------------------------------------------------------------------
df_songs_drake        <- as_tibble(df_songs_drake        %>% select(-c(song_url)))
df_songs_onedirection <- as_tibble(df_songs_onedirection %>% select(-c(song_url)))
df_songs_pink         <- as_tibble(df_songs_pink         %>% select(-c(song_url)))
df_songs_rihanna      <- as_tibble(df_songs_rihanna      %>% select(-c(song_url)))
df_songs_taylorswift  <- as_tibble(df_songs_taylorswift  %>% select(-c(song_url)))
df_songs_u2           <- as_tibble(df_songs_u2           %>% select(-c(song_url)))

# ----------------------------------------------------------------------------

# update the data frames

# ----------------------------------------------------------------------------

df_songs_drake <- as_tibble(df_songs_drake %>% select(-c(song_url)))

df_songs_onedirection <- as_tibble(df_songs_onedirection %>% select(-c(song_url)))

df_songs_pink <- as_tibble(df_songs_pink %>% select(-c(song_url)))

df_songs_rihanna <- as_tibble(df_songs_rihanna %>% select(-c(song_url)))

df_songs_taylorswift <- as_tibble(df_songs_taylorswift %>% select(-c(song_url)))

df_songs_u2 <- as_tibble(df_songs_u2 %>% select(-c(song_url)))

Step 2.04 Merge Data Frames

# ----------------------------------------------------------------------------
# merge the data frames
# ----------------------------------------------------------------------------
df_songs_drake        <- df_songs_drake %>% left_join(df_rankings_drake , by = "song_title")
df_songs_onedirection <- df_songs_onedirection %>% left_join(df_rankings_onedirection, by = "song_title")
df_songs_pink         <- df_songs_pink %>% left_join(df_rankings_pink , by = "song_title")
df_songs_rihanna      <- df_songs_rihanna %>% left_join(df_rankings_rihanna , by = "song_title")
df_songs_taylorswift  <- df_songs_taylorswift %>% left_join(df_rankings_taylorswift , by = "song_title")
df_songs_u2           <- df_songs_u2 %>% left_join(df_rankings_u2 , by = "song_title")

# ----------------------------------------------------------------------------

# merge the data frames

# ----------------------------------------------------------------------------

df_songs_drake <- df_songs_drake %>% left_join(df_rankings_drake , by = "song_title")

df_songs_onedirection <- df_songs_onedirection %>% left_join(df_rankings_onedirection, by = "song_title")

df_songs_pink <- df_songs_pink %>% left_join(df_rankings_pink , by = "song_title")

df_songs_rihanna <- df_songs_rihanna %>% left_join(df_rankings_rihanna , by = "song_title")

df_songs_taylorswift <- df_songs_taylorswift %>% left_join(df_rankings_taylorswift , by = "song_title")

df_songs_u2 <- df_songs_u2 %>% left_join(df_rankings_u2 , by = "song_title")

Step 2.05 Drop Unnecessary Data Frames

Since I no longer need the ranking data frames I will drop them from the global environment.

# ----------------------------------------------------------------------------
# drop the ranking data frames
# ----------------------------------------------------------------------------
rm(list=ls(pattern = "df_rank"))

# ----------------------------------------------------------------------------

# drop the ranking data frames

# ----------------------------------------------------------------------------

rm(list=ls(pattern = "df_rank"))

Step 2.06 Fix the Artist Column

# ----------------------------------------------------------------------------
# populate the missing values
# ----------------------------------------------------------------------------
df_songs_drake        <- df_songs_drake %>% mutate(artist = "Drake")
df_songs_onedirection <- df_songs_onedirection %>% mutate(artist = "One-Direction")
df_songs_pink         <- df_songs_pink %>% mutate(artist = "Pink")
df_songs_rihanna      <- df_songs_rihanna %>% mutate(artist = "Rihanna")
df_songs_taylorswift  <- df_songs_taylorswift %>% mutate(artist = "Taylor-Swift")
df_songs_u2           <- df_songs_u2 %>% mutate(artist = "U2")

# ----------------------------------------------------------------------------

# populate the missing values

# ----------------------------------------------------------------------------

df_songs_drake <- df_songs_drake %>% mutate(artist = "Drake")

df_songs_onedirection <- df_songs_onedirection %>% mutate(artist = "One-Direction")

df_songs_pink <- df_songs_pink %>% mutate(artist = "Pink")

df_songs_rihanna <- df_songs_rihanna %>% mutate(artist = "Rihanna")

df_songs_taylorswift <- df_songs_taylorswift %>% mutate(artist = "Taylor-Swift")

df_songs_u2 <- df_songs_u2 %>% mutate(artist = "U2")

Step 2.07 Merge the Data Frames

Since all of the columns in all of the data frames are same I can use the

# ----------------------------------------------------------------------------
# merge the data frames
# ----------------------------------------------------------------------------
df_songs <- bind_rows(df_songs_drake 
                  ,df_songs_onedirection
                  ,df_songs_pink
                  ,df_songs_rihanna
                  ,df_songs_taylorswift
                  ,df_songs_u2)

df_backup_songs <- df_songs

rm(list=ls(pattern = "df_songs_"))

# ----------------------------------------------------------------------------

# merge the data frames

# ----------------------------------------------------------------------------

df_songs <- bind_rows(df_songs_drake

,df_songs_onedirection

,df_songs_pink

,df_songs_rihanna

,df_songs_taylorswift

,df_songs_u2)

df_backup_songs <- df_songs

rm(list=ls(pattern = "df_songs_"))

Step 2.08 Clean Up and Load Lyrics into Data Frame

Need to clean up the peek_rank column in the data frame. need to remove the hash symbol (#), and phrase ” on”. Updating the column values will allow the column to be converted to an integer.

Next I need to clean up some of the specific song titles. In the next step while creating the models, it won’t like ellipses (“…”) or double quotes for labeling in the model. The song title isn’t part of the model, just used as part of the unique identifier.

Next I created a custom function to get the lyrics from the flat files that I created in the previous step. I then insert the lyrics in to the data frame for each song.

# ----------------------------------------------------------------------------
# convert peek_rank to an integer
# ----------------------------------------------------------------------------
df_songs <- df_songs %>% mutate(peek_rank = stringr::str_replace(peek_rank,"#",""))
df_songs <- df_songs %>% mutate(peek_rank = stringr::str_replace(peek_rank," on",""))
df_songs <- df_songs %>% mutate(peek_rank = as.integer(peek_rank))

# ----------------------------------------------------------------------------
# clean up an invalid song value
# ----------------------------------------------------------------------------
df_songs <- df_songs %>% mutate(song_title = if_else(song_title=="...Ready For It?"
                                          , "Ready For It?" 
                                          , song_title))
df_songs <- df_songs %>% mutate(song_title = if_else(song_title=='The Crystal Ballroom (12" Mix)'
                                                     , "The Crystal Ballroom (12 Mix)" 
                                                     , song_title))

# ----------------------------------------------------------------------------
# get the lyric text from the files
# ----------------------------------------------------------------------------
get_lyric_text <- function(base_dir, song_name){
  lyric_text = read_file(paste0(getwd()
                                , "/lyrics/" ,base_dir ,"_Lyrics/" 
                                , song_name 
                                ,".txt"))
  return(lyric_text)
}
# vectorize the function
get_lyric_text_v <- Vectorize(get_lyric_text)

# load the lyrics into the dataframe
df_songs_lyrics <- df_songs %>% mutate(
                    lyrics = get_lyric_text_v(artist, song_title)
                    )

# ----------------------------------------------------------------------------

# convert peek_rank to an integer

# ----------------------------------------------------------------------------

df_songs <- df_songs %>% mutate(peek_rank = stringr::str_replace(peek_rank,"#",""))

df_songs <- df_songs %>% mutate(peek_rank = stringr::str_replace(peek_rank," on",""))

df_songs <- df_songs %>% mutate(peek_rank = as.integer(peek_rank))

# ----------------------------------------------------------------------------

# clean up an invalid song value

# ----------------------------------------------------------------------------

df_songs <- df_songs %>% mutate(song_title = if_else(song_title=="...Ready For It?"

, "Ready For It?"

, song_title))

df_songs <- df_songs %>% mutate(song_title = if_else(song_title=='The Crystal Ballroom (12" Mix)'

, "The Crystal Ballroom (12 Mix)"

, song_title))

# ----------------------------------------------------------------------------

# get the lyric text from the files

# ----------------------------------------------------------------------------

get_lyric_text <- function(base_dir, song_name){

lyric_text = read_file(paste0(getwd()

, "/lyrics/" ,base_dir ,"_Lyrics/"

, song_name

,".txt"))

return(lyric_text)

}

# vectorize the function

get_lyric_text_v <- Vectorize(get_lyric_text)

# load the lyrics into the dataframe

df_songs_lyrics <- df_songs %>% mutate(

lyrics = get_lyric_text_v(artist, song_title)

)

Step 2.08 Add Feature Columns

Add four additional columns to the data frame

album_decade: The decade the album was released
charted: did the song appear on Billboard’s Hot 100 charts
NumberOne: was the song have a peek ranking of number one on the Billboard Hot 100 chart
chart_group: was the peek ranking of the song in the top 10, or top 100 on the Billboard Hot 100 chart

# ----------------------------------------------------------------------------
# add additional columns to the data
# album_decade: decade album was released
# charted: did the album chart
# numberone: was it a number one song
# chart_group: was it a top ten, 11-100, or not-charted
# ----------------------------------------------------------------------------
df_songs_lyrics <- df_songs_lyrics %>% mutate(album_decade = as.integer(substr(album_year,1,3)) * 10)
df_songs_lyrics <- df_songs_lyrics %>% mutate(charted = ifelse(is.na(peek_rank), "Not Charted" , "Charted"))
df_songs_lyrics <- df_songs_lyrics %>% mutate(NumberOne = ifelse(is.na(peek_rank), FALSE, ifelse(peek_rank == 1, TRUE , FALSE)))
df_songs_lyrics <- df_songs_lyrics %>% mutate(chart_group = ifelse(is.na(peek_rank), "Not Charted"
                                                          , ifelse(peek_rank <= 10, "Top 10" , "Top 100")))

# ----------------------------------------------------------------------------

# add additional columns to the data

# album_decade: decade album was released

# charted: did the album chart

# numberone: was it a number one song

# chart_group: was it a top ten, 11-100, or not-charted

# ----------------------------------------------------------------------------

df_songs_lyrics <- df_songs_lyrics %>% mutate(album_decade = as.integer(substr(album_year,1,3)) * 10)

df_songs_lyrics <- df_songs_lyrics %>% mutate(charted = ifelse(is.na(peek_rank), "Not Charted" , "Charted"))

df_songs_lyrics <- df_songs_lyrics %>% mutate(NumberOne = ifelse(is.na(peek_rank), FALSE, ifelse(peek_rank == 1, TRUE , FALSE)))

df_songs_lyrics <- df_songs_lyrics %>% mutate(chart_group = ifelse(is.na(peek_rank), "Not Charted"

, ifelse(peek_rank <= 10, "Top 10" , "Top 100")))

Step 2.09 Write the Results to a Tab Separated File

Lastly I will save the updated results into a tab separated file to be used in the next step, Model Prediction.

# ----------------------------------------------------------------------------
# save the data frame for future use
# ----------------------------------------------------------------------------

write_tsv(df_songs_lyrics
          ,file.path(paste0(getwd(), "/df_song_lyrics.txt"))
          , na = "NA"
          , append = FALSE
          #, col_names = !append
          , quote_escape = "double"
          )

# ----------------------------------------------------------------------------

# save the data frame for future use

# ----------------------------------------------------------------------------

write_tsv(df_songs_lyrics

,file.path(paste0(getwd(), "/df_song_lyrics.txt"))

, na = "NA"

, append = FALSE

#, col_names = !append

, quote_escape = "double"

)

Trying to share some code ideas

Lyrical Success – Preparing the Data

Part 3 of 4

Step 1 – Clean the Rankings

Step 1.01 Load the Packages

Step 1.02 Load the Ranking Data into Data Frames

Step 1.03 Function to Replace White Space

Step 1.04 Replace the Data Frame

Step 2 – Match the Song Ranks

Step 2.01 Load the necessary packages

Step 2.02 Load the Lyrics into Data Frames

Step 2.03 Update the Data Frames Dropping the song_url Column

Step 2.04 Merge Data Frames

Step 2.05 Drop Unnecessary Data Frames

Step 2.06 Fix the Artist Column

Step 2.07 Merge the Data Frames

Step 2.08 Clean Up and Load Lyrics into Data Frame

Step 2.08 Add Feature Columns

Step 2.09 Write the Results to a Tab Separated File

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Lyrical Success – Preparing the Data

Part 3 of 4

Step 1 – Clean the Rankings

Step 1.01 Load the Packages

Step 1.02 Load the Ranking Data into Data Frames

Step 1.03 Function to Replace White Space

Step 1.04 Replace the Data Frame

Step 2 – Match the Song Ranks

Step 2.01 Load the necessary packages

Step 2.02 Load the Lyrics into Data Frames

Step 2.03 Update the Data Frames Dropping the song_url Column

Step 2.04 Merge Data Frames

Step 2.05 Drop Unnecessary Data Frames

Step 2.06 Fix the Artist Column

Step 2.07 Merge the Data Frames

Step 2.08 Clean Up and Load Lyrics into Data Frame

Step 2.08 Add Feature Columns

Step 2.09 Write the Results to a Tab Separated File

Lyrical Success – Model Prediction

Lyrical Success – Getting the Data

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