vaibhavvesmaker / spotfire

Spotfire

Developing a Random Forest Classifier for Song Likability Prediction Spot-Fire making likability easy

Abstract

In a world where music is a cornerstone of daily life, shaping emotions and experiences, this project leverages the scikit-learn framework to deploy a Random Forest classifier on an extensive Spotify dataset. The aim is to predict song likability based on a diverse array of musical characteristics. Meticulous data preparation, involving encoding variables and normalizing features, is pivotal for honing the model's predictive accuracy. The project navigates the intricacies of feature selection to pinpoint the most influential musical traits influencing likability forecasts. Additionally, a focus on enhancing the model's interpretability aims to unravel the reasoning behind its choices, fostering user trust by revealing essential musical characteristics crucial for accurate likability predictions.

1. Introduction: An unparalleled level of user customization has resulted from the convergence of technology and music in an era dominated by digital streaming services. Accurate song recommendations have become a major focus for music streaming providers as users demand more individualized experiences. To tackle this difficulty, this project explores the field of predictive modeling. Specifically, it uses the scikit-learn module to create a Random Forest classifier, which is then used to a rich dataset obtained from Spotify. 1.1 Background: The way people listen to music has changed dramatically as a result of the widespread use of music streaming services. However, as song libraries develop exponentially, users are left with the difficult process of sorting through an ever-expanding catalog to find new music that suits their tastes. This incites an industry-wide investigation into the creation of recommendation systems capable of perceiving and accommodating personal preferences on an intuitive level.

1.2 Problem Statement: Predicting song likability accurately is the main difficulty this research aims to solve within the broad field of music recommendation. The numerous attributes linked to every song in the Spotify dataset add to the task's difficulty. In addition to being a technological issue, developing a model that can accurately identify consumer preferences from this complex feature set is essential to improving user satisfaction.

1.3 Objectives: This project's main goal is to create a Random Forest classifier that can accurately predict a song's likeability. Reaching this overall objective will need a series of specific milestones, such as figuring out the complexities of data preprocessing, choosing features with care, and deciphering the model's interpretability. The ultimate goal is to improve the user experience on music streaming platforms by providing more precise and customized song recommendations, which go beyond the technical issues.

1.4 Significance of the Project: This project is important because it could help close the gap between computational forecasts and user preferences. Through the utilization of the Random Forest classifier, the research seeks to achieve a fine equilibrium between interpretability and model complexity. The knowledge acquired from this project not only helps to improve music recommendation algorithms but also paves the way for more developments in user-centric content delivery in the future. The following sections of this report will explore the approaches used, experimental setups, findings, and discussions as we set out on this journey of predictive modeling in the context of music likability. Our goal is to give readers a thorough grasp of the procedures followed and the knowledge gained throughout this undertaking.

2. Methodology: 2.1 Data Collection: The first step in this methodology involves the acquisition of a Spotify dataset, a repository with diverse musical features and corresponding likability labels. The dataset summarizes a huge set of dimensions built into each song, including but not limited to acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, and valence. Likability labels, which are very crucial for supervised learning, display the user's subjective preferences for each song. The dataset serves as the base upon which the Random Forest classifier will be trained and evaluated.

2.2 Data Preprocessing: 2.2.1 Handling Missing Values: The integrity of the dataset is of most importance, necessitating a careful approach to address missing values. Imputation techniques such as mean or median substitution for numerical features and mode substitution for categorical features are employed judiciously, striking a balance between preserving the dataset's fidelity and ensuring computational efficiency. 2.2.2 Normalizing Features: The normalization of features is a prerequisite to reducing the impact of scale variations among diverse attributes. This is possible through several techniques like Min-Max scaling or Z-score normalization, ensuring that features contribute uniformly to the modeling process and averting dominance by variables with inherently larger scales. 2.2.3 Encoding Categorical Variables: Given the vast and diverse nature of musical genres and categorical features, the categorical variables become essential. Techniques such as one-hot encoding are applied, transforming the categorical attributes into a format flexible to the Random Forest classifier.

2.3 Feature Selection: A fundamental part of model efficacy is in the selection of features that has significant influence over likability predictions. By using techniques such as Recursive Feature Elimination (RFE) or feature importance analysis, the project navigates the intricate feature space to pick out and retain the salient attributes that contribute substantially to the classifier's predictive power. In our case most pf the features are relevant.

2.4 Model Selection: The comprehensive evaluation of Random Forest and Decision Tree algorithms unequivocally favors Random Forest for song likability prediction. Its ensemble nature, leveraging multiple decision trees, proves superior in performance metrics, mitigating overfitting. Results across accuracy, precision, recall, and F1-score consistently position Random Forest ahead. The ensemble approach excels in discerning critical features, reinforcing its effectiveness in handling complex, non-linear relationships. This establishes Random Forest as the optimal choice for refining personalized music recommendation systems, aligning with its inherent advantages in dataset intricacies and predictive accuracy over standalone Decision Trees. Decision Tree: Random Forest:

2.5 Model Implementation: 2.5.1 Random Forest Classifier: Implementation of the Random Forest classifier is realized through the scikit-learn library, an eminent toolkit in the realm of machine learning. The ensemble nature of Random Forests, comprising a multitude of decision trees, is instantiated to harness the collective predictive prowess of these trees. Parameters such as the number of trees in the forest, maximum depth of the trees, and minimum samples per leaf are carefully tuned through cross-validation to strike an optimal balance between model complexity and generalizability.

Here is the confusion matrix for the Random Forest Model :

2.4.2 Training and Testing: The dataset is partitioned into training and testing subsets to facilitate model evaluation. The Random Forest classifier is trained on the training set, imbibing patterns and relationships within the data. Subsequently, the model is tested on the unseen test set, and performance metrics such as accuracy, precision, recall, and F1-score are computed to gauge the classifier's efficacy in likability prediction. This section summarizes the procedure of supporting the project's methodology, from the foundational data collection to the complex steps of preprocessing, feature selection, and model implementation. The subsequent section will explain the experimental setup, shedding light on the specific configurations employed to rigorously evaluate the performance of the Random Forest classifier in the context of song likability prediction. 3. Implementation Step by Step explanation is as follows: The code begins by importing necessary libraries, including spotipy for interacting with the Spotify API, pandas for handling data, and scikit-learn for machine learning functionalities. Spotify API credentials (client_id and client_secret) are set to authenticate with the Spotify API using the spotipy library. The Spotify dataset is loaded from a CSV file into a Pandas DataFrame (spotify_data). Features (X) and the target variable (Y) are defined by excluding certain columns from the dataset. The dataset is split into training and testing sets using train_test_split from scikit-learn. A Random Forest classifier (rf_model) is instantiated and trained on the training set. The code defines a function get_song_features_by_name that takes a song name as input and uses the Spotify API to retrieve relevant audio features for that song. It searches for the song on Spotify, extracts the track URI, and then retrieves audio features using the audio_features endpoint of the Spotify API. The relevant audio features are then extracted and returned. The code prompts the user to enter the name of a song. It then uses the get_song_features_by_name function to retrieve the relevant features for the user-input song. The features are formatted into a Pandas DataFrame (user_song_data) to match the structure of the training data. The trained Random Forest model is used to predict whether the user might like or dislike the input song. The prediction is displayed to the user.

4. Conclusion: In summary, our project focused on creating a predictive model to improve the music streaming experience. Using a Random Forest classifier and data from Spotify, we aimed to predict song likability. We collected a diverse dataset and went through important steps like data preprocessing, ensuring our dataset was ready for machine learning. Feature selection helped us identify key attributes for likability predictions. Implementing the Random Forest classifier, which is like a team of decision trees, was a crucial milestone. We fine-tuned parameters for optimal performance and extended our project to make dynamic predictions based on user-inputted songs using the Spotify API. In conclusion, our project not only tackled technical challenges but also explored real-time user interaction. The combination of data preprocessing, feature selection, and model implementation demonstrated the effectiveness of Random Forests in addressing real-world challenges in the music streaming landscape. This project showcases the practicality of machine learning in providing personalized and accurate song recommendations, enhancing the user experience in the world of music discovery.

5. Future Scope: Looking ahead, there are exciting opportunities to enhance the impact of this project. Refining feature engineering by adding more meaningful attributes can deepen the model's understanding of user preferences. Exploring advanced modeling techniques like neural networks or alternative ensemble models holds the potential for increased predictive accuracy. The evolution towards dynamic user feedback integration could foster continuous learning and adaptation. Improving model interpretability using techniques like SHAP values or LIME can provide clearer insights into decision-making processes. Expanding the project involves cross-platform integration for a seamless experience across various music streaming services. Developing a user-friendly interface or application can empower users to interact intuitively with the likability prediction system. Collaborative filtering techniques offer an exciting way to incorporate community-driven insights. Integrating multi-modal data, such as user demographics or lyrics analysis, promises to enrich the feature set and elevate prediction accuracy. Rigorous A/B testing methodologies can ensure continuous evaluation and optimization based on user satisfaction metrics. Considerations for scalability and deployment in a production environment are crucial for real-time processing and integration with diverse streaming platforms. These directions not only promise to enhance the sophistication of the recommendation system but also position the project at the forefront of personalized user experiences in the dynamic landscape of music streaming platforms.

References Cited Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression. Outlier Prediction Using Random Forest Classifier. Realization of Random Forest for Real-Time Evaluation through Tree Framing. Interpreting random forest models using a feature contribution method.