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Wishlist_create.py

+import pandas as pd
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load the song data
+song_ratings = pd.read_csv("song_ratings.csv")
+
+# Create a user-song matrix
+user_song_ratings = song_ratings.pivot_table(index='user_id', columns='song_id', values='rating')
+user_song_ratings.fillna(0, inplace=True)
+
+# Calculate the cosine similarity
+song_similarity = cosine_similarity(user_song_ratings.T)
+
+# Define a function to generate a playlist based on a user's wish list
+def generate_playlist(wish_list, num_songs=10):
+    invalid_users = set(wish_list) - set(user_song_ratings.index)
+    if invalid_users:
+        print(f"Song IDs: {invalid_users}")
+        return None
+
+    user_ratings = user_song_ratings.loc[wish_list].values
+    song_ratings_mean = user_song_ratings.mean(axis=0).values.reshape(1, -1)
+    user_ratings_centered = user_ratings - song_ratings_mean
+    song_scores = np.dot(user_ratings_centered, song_similarity) / np.sum(np.abs(song_similarity), axis=0)
+    top_song_indices = np.argsort(-song_scores)[:num_songs]
+    top_song_ids = user_song_ratings.columns[top_song_indices].tolist()
+    return top_song_ids
+
+
+wish_list = [3, 5]
+playlist = generate_playlist(wish_list, num_songs=5)
+if playlist is not None:
+    playlist_songs = song_ratings[song_ratings['song_id'].isin(playlist)]['song_id']
+    print(playlist_songs)

[M.A. Miqdad Ali Riza]

song_ratings.csv

Code.py