# 🎧 **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

![Boxplot](output[2].png)

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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  

![Boxplot](output[3].png)

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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

![Boxplot](output[4].png)

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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  

![Boxplot](output[1].png)

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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.