Jack1808/ML-Music-Classifier-dataset-and-model-name-Models

🎧 Spotify Music Preference Analysis

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🧠 Project Overview

This project analyzes Spotify music data to predict song preferences using machine learning models. The analysis is based on a dataset of 195 songs (100 liked, 95 disliked) with various audio features extracted from Spotify's API.

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📂 Dataset Description

📥 Data Collection Process

Liked Songs (100 tracks):

• 🎵 Primarily French Rap
• 🎸 Some American Rap, Rock, and Electronic music
• ✅ Represents personal music preferences

Disliked Songs (95 tracks):

• 🤘 25 Metal songs (Cannibal Corpse)
• 🎤 20 Rap songs (PNL - personally disliked)
• 🎼 25 Classical songs
• 🕺 25 Disco songs
• 🚫 Pop songs excluded due to neutral preference

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📊 Dataset Structure

The dataset contains 195 rows and 14 columns with the following features:

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🎼 Audio Features Explanation

🔑 Key Predictive Features

• 🎵 Danceability (0.0-1.0)

  • Measures how suitable a track is for dancing
  • Based on tempo, rhythm stability, beat strength, and regularity
  • Higher values indicate more danceable tracks

• ⚡ Energy (0.0-1.0)

  • Represents intensity and activity level
  • Energetic tracks feel fast, loud, and noisy
  • Death metal = high energy, Bach prelude = low energy

• 😊 Valence (0.0-1.0)

  • Musical positiveness measure
  • High valence = happy, cheerful, euphoric
  • Low valence = sad, depressed, angry

• 🎸 Acousticness (0.0-1.0)

  • Confidence measure of acoustic content
  • 1.0 = high confidence the track is acoustic
  • Important for distinguishing electronic vs. acoustic music

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🧩 Secondary Features

• 🗣️ Speechiness (0.0-1.0)

  • Detects spoken words in tracks
  • Above 0.66 = mostly speech (talk shows, audiobooks)
  • 0.33-0.66 = mixed music and speech (rap music)
  • Below 0.33 = mostly music

• 🎼 Instrumentalness (0.0-1.0)

  • Predicts absence of vocals
  • Values above 0.5 likely represent instrumental tracks
  • “Ooh” and “aah” sounds treated as instrumental

• 📢 Liveness (0.0-1.0)

  • Detects audience presence in recordings
  • Above 0.8 = strong likelihood of live performance
  • Useful for distinguishing studio vs. live recordings

• 🔊 Loudness (dB)

  • Overall track loudness in decibels
  • Typically ranges from -60 to 0 dB
  • Useful for comparing relative loudness between tracks

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⚙️ Technical Features

• 🥁 Tempo (BPM)

  • Estimated beats per minute
  • Indicates the speed/pace of the music
  • Directly derived from average beat duration

• 🎹 Key (0-11)

  • Musical key using Pitch Class notation
  • 0 = C, 1 = C♯/D♭, 2 = D, etc.
  • Important for harmonic analysis

• 🔑 Mode (0/1)

  • Musical modality
  • 1 = Major scale, 0 = Minor scale
  • Affects emotional perception of music

• 📏 Time Signature

  • Number of beats per bar/measure
  • Common values: 3 (waltz), 4 (most popular music)

• ⏱️ Duration (ms)

  • Track length in milliseconds
  • Useful for identifying song structure preferences

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📊 Data Visualizations

1️⃣ Box Plot Analysis

• Compares each audio feature between liked (1) and disliked (0) songs
• Helps identify:
  • 🎯 Feature distributions for each preference category
  • ⚠️ Outliers in the data
  • 🚦 Clear separations between liked and disliked songs
  • 🧪 Discriminative features

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2️⃣ Correlation Heatmap

Reveals:

• 🔁 Relationships between audio features
• 🧬 Multicollinearity issues (highly correlated features)
• 🔍 Feature independence for model selection
• 🛠️ Feature engineering opportunities

Key insights:

• Energy and loudness → strong positive correlation
• Acousticness and energy → negative correlation
• Valence → emotional interpretation link

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3️⃣ Pair Plot Analysis

• Shows relationships between: danceability, energy, valence, tempo, acousticness
• Detects:
  • 🎯 Clustering patterns for liked vs. disliked
  • 🤝 Feature interactions
  • 📈 Distribution shapes

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4️⃣ Model Performance Comparison

Models Evaluated:

• ✅ Logistic Regression
• 🌲 Random Forest
• 🧠 Neural Network
• 📍 K-Nearest Neighbors (KNN)
• 🛠️ Artificial Neural Network (ANN)
• 📐 Support Vector Machine (SVM)
• 📊 Naive Bayes
• 🚀 XGBoost

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🧠 Key Insights

✅ Most Important Features:

• 😊 Valence – correlates with emotion
• ⚡ Energy – identifies genre intensity
• 💃 Danceability – relates to rhythm preference
• 🎸 Acousticness – distinguishes acoustic/electronic
• 🗣️ Speechiness – useful for rap detection

❌ Least Important Features:

• ⏱️ Duration – length is less relevant
• 🎹 Key – minimal influence on likability
• 📏 Time Signature – usually standard
• 📢 Liveness – studio vs. live not crucial

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🧪 Model Performance Insights

• 🌲 Ensemble methods (Random Forest, XGBoost) perform best
• 🧠 Neural Networks capture complex patterns
• 🧮 Simple models like Logistic Regression provide baselines
• 📊 Performance differences highlight feature complexity

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⚙️ Technical Implementation

🧼 Data Processing

• 🎯 Feature Extraction – Spotify API (audio features)
• 🧹 Data Cleaning – handle nulls/outliers
• 📐 Feature Scaling – normalize ranges
• 🔀 Train-Test Split – measure generalization

📏 Model Evaluation

• 🎯 Accuracy – main performance metric
• 🔁 Cross-Validation – stability check
• 📊 Feature Importance – understand model decisions
• 🧩 Confusion Matrix – analyze misclassifications

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💡 Applications

• 🎧 Personal Music Recommendation Systems
• 🎶 Understanding Music Preference Patterns
• 📝 Automated Playlist Generation
• 📈 Music Streaming Service Improvements
• 📊 Market Research for Music Industry

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🚀 Future Enhancements

• 📈 Larger Dataset – more genre diversity
• 🧩 Additional Features – genre, artist, release year
• 🤖 Advanced Models – deep learning (e.g. RNN, CNN)
• 🌐 Real-Time Prediction – stream-integrated recommendations
• 🧍 Collaborative Filtering – user-based recommendations

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📎 Dataset

📁 File: [data.csv] Content: Contains 195 rows and 14 audio features used for analysis and modeling.

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🧠 This analysis demonstrates how machine learning can understand and predict personal music preferences using audio features extracted from Spotify's comprehensive music database.