Spotify-and-YouTube-Analysis-using-Machine-Learning-Algorithms

In this project, I implemented various Machine Learning Algorithms to analysis the data that I got from https://www.kaggle.com/datasets/salvatorerastelli/spotify-and-youtube.

Initial Proposal: The selected dataset contains information about songs from various artists worldwide, focusing on their presence on Spotify and YouTube. Each song's data includes Spotify-specific statistics like the number of streams and YouTube-specific data such as the number of views for the official music video. The dataset comprises 26 variables for each song, offering detailed insights into the music and its online presence. These variables include the song's name, artist, URL on Spotify, album, album type, and various audio features like danceability, energy, key, loudness, liveness, valence, tempo, duration, etc. Additionally, the dataset provides information about the song's performance on YouTube, such as its title, channel, views, likes, comments, description, licensing status, and whether the video is the official one. I want to work on a project that will help people find music they love. I'm using a big dataset from Spotify and YouTube to make personalized song recommendations based on what people have listened to before. But I'm also looking at how popular songs change over time. Do certain songs become more popular during different seasons or times of the year? I'm going to use numbers and patterns to try to predict what songs might become popular in the future. So, I'm not just making playlists; I'm also figuring out how music changes over time.

Algorithms include:

Linear Regression:
- Purpose: Predicts a continuous outcome variable based on one or more predictor variables.
- Application: Used in regression analysis to understand the relationship between variables.
Neural Networks:
- Purpose: Models inspired by the human brain, comprising interconnected nodes (neurons) that process information.
- Application: Used for complex pattern recognition, image and speech recognition, and various machine learning tasks.
Multi-Layer Perceptron (MLP):
- Purpose: A type of neural network with multiple layers, including an input layer, hidden layers, and an output layer.
- Application: Suitable for tasks requiring non-linear mapping between inputs and outputs.
Linear Discriminant Analysis (LDA):
- Purpose: Dimensionality reduction technique that finds the linear combinations of features that best separate classes.
- Application: Used in classification problems to improve computational efficiency and reduce overfitting.
Principal Components Analysis (PCA):
- Purpose: Reduces the dimensionality of data while retaining most of its variability.
- Application: Commonly used for feature extraction and visualization.
Multi-Dimensional Scaling:
- Purpose: Visualizes the pairwise dissimilarity between data points in a reduced-dimensional space.
- Application: Used for exploring relationships and similarities in high-dimensional data.
Gaussian Mixture Models:
- Purpose: Probabilistic model representing a mixture of Gaussian distributions.
- Application: Used for clustering and density estimation in unsupervised learning.
Support Vector Machine (SVM):
- Purpose: Classifies data points by finding the hyperplane that best separates different classes.
- Application: Effective for both classification and regression tasks, particularly in high-dimensional spaces.
Decision Trees:
- Purpose: Represents decisions and their possible consequences in a tree-like structure.
- Application: Widely used for classification and regression tasks, easily interpretable.
K-Means:
- Purpose: Clustering algorithm that partitions data into k clusters based on similarity.
- Application: Used for grouping data points into clusters based on similarity in features.

Physic1990/Spotify-and-YouTube-Analysis-using-Machine-Learning-Algorithms

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