| # 🎧 **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. | |