🎵 Hit Song Prediction with Machine Learning

📌 Project Overview

This project aims to predict whether a song will be a hit or not based on its audio features from Spotify.
A song is classified as a hit if its popularity score is greater than 50.

We use Random Forest Classifier, a powerful machine learning model, to perform this classification.

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📊 Dataset Information

The dataset consists of 114,000 songs with the following features:

Column Name	Description
track_id	Unique Spotify ID for each track
artists	Names of the artists who performed the track
album_name	Name of the album in which the track appears
track_name	Title of the track
popularity	Popularity score (0-100)
duration_ms	Length of the track in milliseconds
explicit	Boolean (1 = explicit, 0 = clean)
danceability	Suitability of a track for dancing (0.0 - 1.0)
energy	Intensity and activity of a track (0.0 - 1.0)
key	Musical key of the track
loudness	Overall loudness in decibels (dB)
mode	Modality (1 = Major, 0 = Minor)
speechiness	Presence of spoken words (0.0 - 1.0)
acousticness	Acoustic confidence (0.0 - 1.0)
instrumentalness	Probability of a track being instrumental (0.0 - 1.0)
liveness	Presence of an audience (0.0 - 1.0)
valence	Positivity of the track (0.0 - 1.0)
tempo	Estimated tempo (beats per minute - BPM)
time_signature	Estimated time signature (3 to 7)

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🎯 Goal: Predicting Hit Songs

We define a hit song as one with a popularity score > 50.
Thus, we create a new binary target variable:

• hit = 1 → If popularity > 50
• hit = 0 → Otherwise

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⚙️ Methodology

1️⃣ Data Preprocessing

• Numerical values are scaled for better model performance.
• The dataset is split into training (80%) and testing (20%) sets.

2️⃣ Model Selection & Training

• A Random Forest Classifier is used to classify songs as "hit" or "not hit".
• The model learns patterns from features like danceability, energy, valence, tempo, etc.

3️⃣ Model Evaluation

• The accuracy of the model is measured.
• Feature importance is analyzed to see which factors influence a hit song the most.

4️⃣ Hyperparameter Tuning

• Grid Search is used to optimize model parameters for better accuracy.

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🚀 How to Run the Project

Hit Song Prediction

1️⃣ Clone the Repository and Install Dependencies

git clone https://github.com/yourusername/HitSongRandomForest.git
cd HitSongRandomForest
pip install -r requirements.txt

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License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.