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https://github.com/archanakokate/kkbox_music_recommendations

Predicting the chances of a user listening to a song repetitively after the first observable listening event.
https://github.com/archanakokate/kkbox_music_recommendations

data-visualization exploratory-data-analysis machine-learning statistical-analysis

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Predicting the chances of a user listening to a song repetitively after the first observable listening event.

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## KKBox Music Recommendations



The primary goal of this project is to develop a robust predictive model that accurately identifies the likelihood of users listening to a song repetitively on KKBOX. By understanding user behavior and preferences, we aim to improve the music recommendation system and enhance the overall user experience on the platform.



The dataset is from KKBOX, Asia’s leading music streaming service, holding the world’s most comprehensive Asia-Pop music library with over 30 million tracks.

In this task, we predict the chances of a user listening to a song repetitively after the first observable listening event within a time window was triggered. If there are recurring listening event(s) triggered within a month after the user’s very first observable listening event, its target is marked 1, and 0 otherwise in the training set. The same rule applies to the testing set.



KKBOX training data set consists of information of the first observable listening event for each unique user-song pair within a specific time duration. Metadata of each unique user and song pair is also provided. The train and the test data are selected from users listening history in a given time period.



### Dataset link: 

  https://www.kaggle.com/c/kkbox-music-recommendation-challenge/data



### Dataset Description



### 1) train.csv

  msno: user id

  song_id: song id

  source_system_tab: the name of the tab where the event was triggered. System tabs are used to categorize KKBOX mobile apps functions. 

  source_screen_name: name of the layout a user sees.

  source_type: an entry point a user first plays music on mobile apps. An entry point could be album, online-playlist, song .. etc.

  target: this is the target variable. target=1 means there are recurring listening event(s) triggered within a month after the user’s very first observable listening event, target=0 otherwise .



### 2) test.csv

id: row id (will be used for submission)

  msno: user id

  song_id: song id

  source_system_tab: the name of the tab where the event was triggered. 

  source_screen_name: name of the layout a user sees.

  source_type: an entry point a user first plays music on mobile apps. An entry point could be album, online-playlist, song .. etc.



### 3) songs.csv

  song_id

  song_length: in ms

  genre_ids: genre category. Some songs have multiple genres and they are separated by |

  artist_name

  composer

  lyricist

  language

  

### 4) members.csv

  msno

  city

  bd: age. Note: this column has outlier values, please use your judgement.

  gender

  registered_via: registration method

  registration_init_time: format %Y%m%d

  expiration_date: format %Y%m%d

  song_extra_info.csv



### 5) song_id

  song name - the name of the song.

  isrc - International Standard Recording Code, theoretically can be used as an identity of a song.  



## Key tasks performed :



#### Data Loading and Exploration: 

- Unzipped and loaded all datasets.

- Merged datasets and explored them to gain insights into the data structure and contents.

#### Data Preprocessing: 

- Handled missing values appropriately.

- Converted date-related columns to proper date formats.

#### Feature Engineering: 

- Extracted meaningful features from relevant columns.

- Merged relevant features from different datasets to create a consolidated dataset.

#### Data Preparation for Modeling:

- Encoded categorical features using Label Encoding.

- Standardized the data using Standard Scaler

#### Model Building, Training and Model Evaluation:

- Splitted training data into training and validation sets.

- Used Decision Tree model for binary classification to train the data.

- Evaluated the model's performance on the validation set using metrics like classification report, confusion matrix, accuracy and cross validation scores.

#### Music Recommendation:

- Used the trained model to predict the target variable (recurring listening events) on the test dataset.