Last.fm is a music discovery service that gives you personalised recommendations based on the music you listento.
Here we are going to do some machine learning and data anlysis on the dataset of last.fm inorder to recommend the next songs to the user.
We are going to use NearestNeighbors Algorithm to predict next songs that user will like to hear
Note: Dataset retrieved Last.fm [LastFM_Matrix.csv] contaning 1257 records and 285 Songs
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# First Import some essential Libraries
import os
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity # For calculating similarity matrix
from sklearn.neighbors import NearestNeighbors
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DIR_PATH = os.getcwd() #Get currect directory
lfm = pd.read_csv(DIR_PATH + "//LastFM_Matrix.csv") #Load dataset
lfm.head() #Display Head of the dataset
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lets get all/some names of songs and user coloumn in the dataset
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songs = pd.DataFrame(lfm.columns)
songs.head(10)
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Now let's import only songs and make a new DataFrame
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lfm_songs = lfm.drop("user",axis =1) #drop user column
lfm_songs.head() # Show Head
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lfm_songs.shape #gives out total rows and columns
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Calculate cosine_similarity in order to get Similarity Matrix
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data_similarity = cosine_similarity(lfm_songs.T) #
data_similarity
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Now we have obtained data similarity matrix now lets use K-nearest neighbour algo and predict the recommendations but first we will label the matrix
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type(data_similarity)
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Lets convert it ito DataFrame
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data_similarity_df = pd.DataFrame(data_similarity, columns=(lfm_songs.columns), index=(lfm_songs.columns))
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data_similarity_df.head()# similarity Matrix
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data_similarity_df.index.is_unique # check if there is no repeated songs
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Now we will use NearestNeighbors Algorithm and apply to similarity matrix to get the recommendation
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neigh = NearestNeighbors(n_neighbors=285)
neigh.fit(data_similarity_df) # Fit the data
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#Copy the predicted data to a new DataFrame
model = pd.DataFrame(neigh.kneighbors(data_similarity_df, return_distance=False))
model.head() #gives you integer values instead of song names
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final_model = pd.DataFrame(data_similarity_df.columns[model], index=data_similarity_df.index)#gives names with respect to songs
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final_model.head() #preview final Model
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The above model gives us all 285 Recommendation, but we want only Top 10 recommendation, so lets modify the DataFrame a bit
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top10 = final_model[list(final_model.columns[:11])]
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top10.head()
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Now lets put our results in CSV File called top10
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top10.to_csv("top10.csv",index_label = "Index") # store data in csv file
Now lets read the CSV File to check if its saved or not
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pd.read_csv("top10").head()
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To conclude we have created a model which recommends next song user will like to hear by using last.fm data.
Further we can now use this model to make an API and use it in our Website or WebApp to recommend songs to the user.
Github Link : https://github.com/kartikjagdale/Last.fm-Song-Recommender
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