Read Me Spotify Popularity Prediction Analysis This project involves predicting the popularity of songs on Spotify using Python and Jupyter Notebooks. The analysis utilizes three different regression models: Linear Regression, K-Nearest Neighbors (KNN) Regressors, and Support Vector Machines (SVM).

Models Used Linear Regression: A straightforward approach that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation. K-Nearest Neighbors (KNN) Regressors: A non-parametric method that predicts the value of a target variable based on the average of its k-nearest neighbors in the feature space. Support Vector Machines (SVM): A powerful and flexible method that performs regression by finding the hyperplane that best fits the data, capable of handling both linear and non-linear relationships. Dataset Information The dataset used for this analysis is sourced from Kaggle and contains detailed information on various attributes of songs from 1921 to 2020. Due to the size of the dataset, the actual file is not included in this repository. However, it is continuously updated and can be accessed via the link below. The dataset includes a range of columns such as:

Acousticness: A measure of whether a track is acoustic. Artists: The performing artists. Danceability: Describes how suitable a track is for dancing. Duration_ms: The duration of the track in milliseconds. And more: Including features like energy, instrumentalness, liveness, loudness, speechiness, tempo, and valence. Data Source For more details and to access the dataset, please visit Kaggle: Kaggle.com - Spotify Dataset (1921-2020) - 160k Tracks