Personalised-Song-Playlist-Generator

by Arka Sarkar, Pankil Kalra and Daksh Thapar, Machine Learning (CSE343, ECE343) from Indraprastha Institute of Information Technology, Delhi.

Introduction

With the growing popularity of music streaming services like Spotify, Apple Music and Wynk, the number of songs have skyrocketed globally. Creating personalized playlists for users has become tedious and challenging as it involves individual listening to various songs and categorizing them based on their audio features. The objective is to sort songs with similar musical characteristics into playlists automatically. Modern machine learning techniques and visualization tools should help us find accurate models that categorize millions of songs into user playlists based on song choices. Related works on this problem did not consider Lyrical Analysis while making playlists. We are using Topic Modelling techniques on lyrics, and will be using the extracted topics as a feature for generating playlists.

Description and Implementation Details

In this repository, we have implemented vairous machine learning aglorithms on Topic Extracted from the lyrical corpus.

• DataScrape - Dataset, Data Collecction code, pre-processing and topic modeling
  
• PreProcessing - Code for Visualisation
• models - binary classification , multi classification , clustering, K-nearest Neighbours source code with the respective models saved with the results also saved.

How to Run ?

The models pickles are saved in "rb" format in their respeective directories (\models directory).

• To load the models

  f = open("model_name", "rb")
  load_model = pickle.load(f)
  f.close()
  

• To test the models

  • Clustering Models - They are simple pickled files of the models which can be executed and evaluated by the following code .

    model.predict(X_test)
    

  • Multi classification Models - hey are simple pickled files of the models which can be executed and evaluated by the following code.

    model.predict(X_test)
    

  • Binary Classification Models - It is a list of all the one vs all models which can be executed and evaluated using the following code.

     pred = []
     for i in range(len(load_model)):
         pred.append(load_model[i].predict(X_test))
    
     pred.argmax(axis = 1)
    

• Generate Graphs for the Analysis

  • Each graph can be plotted from the ipynb files in the directories, the code is documented for one to understand and plot the respective graphs.

Installed dependencies

python 3.7
jupyter notebook.
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import balanced_accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import recall_score
from tqdm import tqdm
from imblearn.ensemble import BalancedBaggingClassifier
from imblearn.under_sampling import RandomUnderSampler
from sklearn.svm import SVC
from xgboost import XGBClassifier
import pickle
import tensorflow as tf
import math
import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn import preprocessing
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from matplotlib import pyplot as plt
from sklearn.metrics import silhouette_samples
import matplotlib.cm as cm
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import seaborn as sns
from sklearn.decomposition import TruncatedSVD
from sklearn.cluster import DBSCAN
from sklearn.metrics import silhouette_score
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import SpectralClustering
import pyLDAvis
import pyLDAvis.gensim

Contact

For any furhter queries feel free to reach out the following contributors.

Pankil Kalra ([email protected])
Daksh Thapar ([email protected])
Arka Sarkar ([email protected])

Final Report