dattadeepayan / spotify-stream-trend

ML Project Fall 2021.

spotify-stream-trend

ML Project Fall 2021.

Data

The 'data' folder consists of

1. /8countries

• Preprocessed data for 8 countries.

2. /9countries

• top200_9countries.csv

  • Removed unwanted columns ('Unnamed: 0','artist_x', 'artist_y', 'explicit', 'type', 'chart', 'album', 'id')
  • Concatenated title + region = title
  • Split date => year and month.
  • Added top200 count (across full data) - based on title (which has region).
  • Calculated trend_rank_score for a song based on region (title), up till each individual date (sorted data by AO of title, date).

• top200_9countries_processed2.csv

  • Reduced dataset to most recent unique songs (by region).
  • Merged trend_rank_score into this.
  • Binned streams by different regions.

3. /ie

• Full data (meta + audio features + charts) for Ireland.

Time Series Analysis

'EDA+TimeSeries.ipynb'contains the code for the following:

• Data Extraction
• Data Preprocessing
• Data Augmentation(inclusion of features top200_count and trend_rank_score)
• Categorical Feature Encoding
• Time-Series Models: ARIMA(Auto Regressive Integrated Moving Average) and LSTM RNN(Long Short Term Memory Recurrent Neural Networks)

The timeseries models were run on "acousticness" features of the Rank #1 song in the Canadian top200 charts, sampled in 30-day intervals. We have also provided the flexibility to enable the user to run the models for different combination of inputs: Rank, Feature and Country.

We have observed that the ARIMA model performed better to forecast acousticness on our Canadian dataset (univariate) than its LSTM counterparts (vanilla, multivariate and mulilayer).

Classification

'Classification_models.ipynb' contains the code to classify a song for a particular region on a given month and a given year into one of the stream bins (1,2,3,4).

Where 1 signifies that the song for a particular region will have streams in the range (minimum streams, 25 percentile of streams), 2 signifies that the song will have song will have streams in the range (25 percentile, 50 percentile of the streams), 3 signifies that the song will have streams in the range (50 percentile, 75 percentile of the streams) and 4 signifies that the song will have streams in the range (75 percentile, maximum streams).

The different classification models implemented are: Decison trees, K Nearest Neighbours, Random forest and Multi Layer Perceptron.

Multilayer Perceptron gave the best performance amongst all the classification models with a weighted-f1 score of 0.712.