What is Spotify? Spotify is a audio and media streaming service that offer a variety of plans to suit your listening needs. This service has over 365 million monthly active users with over 70 million songs to stream.
Our goal with this project is to create an interactive app where a user inputs their favorite song/artist. Based on that input feedback is given to recommend similar songs to their input....to the nearest 5 songs.
Click the link above to view the spotify song suggestor...Music awaits get groovy!
After clicking the link to the app, navigate to the search engine, from that search engine you will then enter your favorite artist or song. After inputing your results you will now discover new songs to the nearest five as well check the metrics of each song, with those metrics 5 songs to the nearest metrics entered will then give back suggestions.
Data was collected via kaggle from this link. With the use of the Nearest Neigbor model, and an API, our app will make predictions to the nearest five similar songs of your likings.
Libraries used: Pandas, Numpy, Sklearn, Pickle,
How the dataset was wrangled:
def wrangle(df):
# Drop columns not in use by nearest-neighbors
df.drop(columns=['type', 'id', 'track_href', 'analysis_url', 'title', 'Unnamed: 0'], inplace=True)
# Drop unuseful audio features
df.drop(columns=['instrumentalness', 'time_signature'], inplace=True)
# Drop genre, might not work well with nearest-neighbors and does not appear in spotify api request
df.drop(columns=['genre'], inplace=True)
# Making a pool of songs to use as query items
test_df = df[df['song_name'].isna() == True]
# Dropping rows without song_names, maybe we can keep them if we implement the api calls
df = df[df['song_name'].isna() == False]
# Drop song-name, not used in nearest-neighbors
df.drop(columns=['song_name'], inplace=True)
test_df.drop(columns=['song_name'], inplace=True)
return dfUse of standard scalar to remove mean and scale it to unit variance for each feature
scaler = Normalizer()
scaler.fit(uq_df)
scaled_df = scaler.transform(uq_df)Initiate Nearest Neighbor Estimator
# Instantiate nearest-neighbors estimator, n_neighbors is Number of neighbors to use by default for kneighbors queries.
nn = NearestNeighbors(n_neighbors=5)
# fit to our song's audio features
nn.fit(scaled_df)Nearest Neighbor Model Trained and Fitted
# NearestNeighbors is the model that is going to give us a list of the most
# similar songs to the searched song
nn = NearestNeighbors(n_neighbors=6)
nn.fit(X_train)
# This shows us what songs are similar by index
doc_index = 5
doc = [X_train[doc_index]]
# Query using kneighbors
neigh_dist, neigh_index = nn.kneighbors(doc)MIT License
Copyright (c) 2021 Fadil Shaikh, Jafar Sakha, Mikayla Kosmala, Mohamed Mosaed, Royce Roberts
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