# 🎵 Spotify Song Preference Dataset

This dataset contains Spotify audio features for 195 songs categorized as **liked** or **disliked** by the user. It was created to build and train ML models that can predict user preferences in music based on quantitative audio features.

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## 📥 Dataset Overview

- **Total songs**: 195
- **Format**: CSV (`data.csv`)
- **Source**: Spotify API
- **Target column**: `liked` (1 = liked, 0 = disliked)
- **Data type**: Tabular
- **Licensing**: For academic and personal research use (derived from Spotify API)

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## 📦 Dataset Composition

| Category     | Count | Description                                 |
|--------------|-------|---------------------------------------------|
| Liked        | 100   | Mostly French/American rap, rock, electro   |
| Disliked     | 95    | 25 metal, 25 classical, 25 disco, 20 rap    |
| Neutral (Pop)| ❌ Not included (user is neutral)                   |

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## 🧪 Features

Extracted via Spotify API – "Get Audio Features for Several Tracks"

| Feature           | Description                                                                 |
|-------------------|-----------------------------------------------------------------------------|
| `danceability`     | How suitable the track is for dancing (0–1)                                |
| `energy`           | Perceived intensity (0–1)                                                  |
| `key`              | Musical key (0 = C, 1 = C♯/D♭...)                                           |
| `loudness`         | Overall volume in dB (-60 to 0)                                            |
| `mode`             | 1 = major, 0 = minor                                                       |
| `speechiness`      | Detects presence of speech (0–1)                                           |
| `acousticness`     | Confidence measure of being acoustic (0–1)                                 |
| `instrumentalness` | Predicts presence of vocals (0–1)                                          |
| `liveness`         | Live audience presence (0–1)                                               |
| `valence`          | Positiveness of the song (0–1)                                             |
| `tempo`            | Beats per minute (BPM)                                                     |
| `duration_ms`      | Duration of the song in milliseconds                                       |
| `time_signature`   | Estimated time signature (e.g. 4 = 4/4)                                    |
| `liked` (target)   | 1 = liked, 0 = disliked                                                    |

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## 🔍 Exploratory Data Analysis (EDA)

### ✅ 1. Missing Values
- No missing values found

### ✅ 2. Class Distribution
- Liked (1): 100 songs
- Disliked (0): 95 songs
- **Class is balanced**

### ✅ 3. Data Types
- All features are numerical
- Target (`liked`) is binary

### ✅ 4. Summary Statistics
- Energy, Danceability, Valence tend to be higher for liked songs
- Acousticness and Instrumentalness higher in disliked songs

### ✅ 5. Correlation Matrix
- Strong positive correlation: `energy` ↔ `loudness`
- Negative correlation: `acousticness` ↔ `energy`, `valence`

### ✅ 6. Visual Highlights (Suggested)
- **Boxplots**: `energy`, `danceability` by `liked`
- **Countplot**: class balance of `liked`
- **Heatmap**: correlation of features
- **Scatter**: `energy` vs `valence` colored by `liked`

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## 🤖 ML Use Cases

You can use this dataset to train:
- Logistic Regression
- Random Forest
- KNN / SVM
- ANN / XGBoost / LightGBM
- Naive Bayes

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## 📊 Visualizations

### 1. Boxplot: Energy Distribution by Liked
This shows how energy values are distributed for liked and disliked songs.
![Energy Boxplot](boxplot_energy.png)

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### 2. Boxplot: Danceability Distribution by Liked
This shows how danceability varies between liked and disliked songs.
![Danceability Boxplot](boxplot_danceability.png)

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### 3. Scatter Plot: Energy vs Valence
This plot helps visualize clusters or spread of liked vs disliked songs based on energy and valence.
![Energy vs Valence](scatter_energy_valence.png)

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### 4. Correlation Heatmap
This heatmap shows how all audio features correlate with each other.
![Correlation Heatmap](correlation.png)

### 5. Countplot: Liked vs Disliked Songs

This chart shows the number of songs in each class: `0 = Disliked`, `1 = Liked`.  
It confirms that the dataset is nearly balanced.

![Liked vs Disliked Countplot](countplot_liked.png)


## 📉 Statistical Testing

To determine which features are statistically different between liked and disliked songs, a two-sample t-test was performed using:

| Feature          | p-value | Significance      |
| ---------------- | ------- | ----------------- |
| danceability     | 0.0000  | ✅ Significant     |
| energy           | 0.0159  | ✅ Significant     |
| key              | 0.5371  | ❌ Not significant |
| loudness         | 0.0000  | ✅ Significant     |
| mode             | 0.7418  | ❌ Not significant |
| speechiness      | 0.0000  | ✅ Significant     |
| acousticness     | 0.0134  | ✅ Significant     |
| instrumentalness | 0.0000  | ✅ Significant     |
| liveness         | 0.8924  | ❌ Not significant |
| valence          | 0.0002  | ✅ Significant     |
| tempo            | 0.0000  | ✅ Significant     |
| duration_ms     | 0.0000  | ✅ Significant     |
| time_signature  | 0.0023  | ✅ Significant     |


🔍 Observation:
    - Features with p-value > 0.05 are statistically insignificant

    - These features do not show a meaningful difference between liked and disliked songs

    - We can safely remove the following features:
        liveness
        mode
        key

✅ This simplifies the dataset and improves model performance by removing noise.


## 📈 Model Accuracy Comparison

Bar chart showing accuracy of different models used in the project.

![Model Accuracy Chart](accuracy_comparison.png)