CoolFace
Datasetpublic

Ohad777/spotify-hit-prediction-analysis

Your browser does not support the video tag. 🎵 Spotify Hit Prediction - Exploratory Data Analysis (EDA) Project Overview This project analyzes audio features from Spotify to predict track popularity. Using a sample of 2,000 tracks, I explored how technical attributes like energy and danceability relate to a song's success. 🔍 Research Questions & Insights I addressed several key questions during the EDA: Is the data balanced? I analyzed… See the full description on the dataset page: https://huggingface.co/datasets/Ohad777/spotify-hit-prediction-analysis.

sourceHugging Facemitupdated 6mo agoView on Hugging Face
0likes64downloads
Dataset Card

<video controls style="max-width: 100%;"> <source src="https://huggingface.co/datasets/Ohad777/spotify-hit-prediction-analysis/resolve/main/presentation.mp4" type="video/mp4"> Your browser does not support the video tag. </video>

🎵 Spotify Hit Prediction - Exploratory Data Analysis (EDA)

Project Overview

This project analyzes audio features from Spotify to predict track popularity. Using a sample of 2,000 tracks, I explored how technical attributes like energy and danceability relate to a song's success.

🔍 Research Questions & Insights

I addressed several key questions during the EDA:

  1. 1.Is the data balanced? I analyzed the ratio of popular vs. non-popular tracks to ensure fair modeling.
  2. 2.Energy vs. Loudness: Confirmed a strong positive correlation (0.79), showing energetic tracks are consistently louder.
  3. 3.Does "Happiness" matter? Using a Violin Plot, I found that both sad and happy songs (Valence) can become hits.
  4. 4.Danceability: Popular tracks tend to have a slightly higher and more consistent danceability range.
  5. 5.Tempo: Found no significant linear relationship between BPM and popularity.

🛠️ Data Decisions

  • —Sampling: Worked with 2,000 rows for efficiency.
  • —Target: Created a binary variable is_popular (1 for Popularity > 50, 0 otherwise).
  • —Cleaning: Confirmed zero missing values and decided to keep outliers as genuine musical variations.

📁 Files

  • —spotify_sample_2000.csv: Processed data subset.
  • —Ohad_Danon_Assignment_1_EDA_&_Dataset.ipynb: Full analysis code and visualizations.