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df_party.boxplot(vert = False, figsize = (13,7), showfliers = False, showmeans = True, 
                 patch_artist=True, boxprops=dict(linestyle='-', linewidth=1.5),
                 flierprops=dict(linestyle='-', linewidth=1.5),
                 medianprops=dict(linestyle='-', linewidth=1.5),
                 whiskerprops=dict(linestyle='-', linewidth=1.5),
                 capprops=dict(linestyle='-', linewidth=1.5))

plt.title("Original Playlist's Box Plot", fontsize=16, fontweight='heavy')
plt.show()

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df_dinner = fetch_audio_features(sp, username, '37SqXO5bm81JmGCiuhin0L')
df_party = fetch_audio_features(sp, username, '2m75Xwwn4YqhwsxHH7Qc9W')
df_lounge = fetch_audio_features(sp, username, '6Jbi3Y7ZNNgSrPaZF4DpUp')
df_pop = fetch_audio_features(sp, username, '3u2nUYNuI08yUg877JE5FI')

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# Take a sample from the Pop playlist
df_pop_sample_I = df_pop.sample(n=40, weights='danceability', random_state=1)
df_pop_sample_I.describe()

# Concatenate the original playlist with the sample
df_party_exp_I = pd.concat([df_party, df_pop_sample_I])
df_party_exp_I.describe()

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# Take a sample from the Pop playlist
df_pop_sample_II = df_pop[(df_pop['danceability'] > 69.55) & (df_pop['valence'] > 51.89)].copy()

# Concatenate the original playlist with the sample
df_party_exp_II = pd.concat([df_party, df_pop_sample_II])
df_party_exp_II.describe()

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# Take a sample from the Pop playlist
df_pop_sample_III = df_pop[df_pop['score'] > df_party['score'].mean()].copy()

# Concatenate the original playlist with the sample
df_party_exp_III = pd.concat([df_party, df_pop_sample_III])
df_party_exp_III.describe()

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# Make a temporary list of tracks
list_track = df_party_exp_III.index

# Create the playlist
enrich_playlist(sp, username, '779Uv1K6LcYiiWxblSDjx7', list_track)

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# Read in data
spam_collection = pd.read_csv('SMSSpamCollection', sep='\t', header=None, names=['Label', 'SMS'])

# Randomize the data set
randomized_collection = spam_collection.sample(frac=1, random_state=3)

# Calculate index for the split-up
training_test_index = round(len(randomized_collection) * 0.8)

# Training/Test split-up

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# Import libraries
import numpy as np
import pandas as pd
import re
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from kneed import KneeLocator
from sklearn.linear_model import LogisticRegression

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### While in Python environment ###
import nltk

# Download stopwords
nltk.download(‘stopwords’)

# Download punkt sentence tokenizer
nltk.download(‘punkt’)

# Download wordnet

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# Install Requests
pip3 install requests

# Install Kneed
pip install kneed