# 🎵 Music Feature Dataset Analysis

Welcome to the Exploratory Data Analysis (EDA) of a music feature dataset. This project aims to uncover insights into how audio features of songs influence user preferences, using the `liked` column as the target variable.

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## 📁 Dataset Overview: `train.csv`

This dataset contains various musical/audio features for tracks, along with a `liked` score representing user preference.

### 🎼 Features Description

| Feature           | Description                                                           |
|-------------------|------------------------------------------------------------------------|
| `danceability`    | Suitability of a track for dancing.                                    |
| `energy`          | Intensity and activity level of the track.                             |
| `key`             | Musical key as an integer (0 to 11).                                   |
| `loudness`        | Overall loudness in decibels (dB).                                     |
| `mode`            | Tonality: major (1) or minor (0).                                      |
| `speechiness`     | Presence of spoken words in the track.                                 |
| `acousticness`    | Likelihood the track is acoustic.                                      |
| `instrumentalness`| Predicts whether a track contains no vocals.                           |
| `liveness`        | Presence of an audience in the recording.                              |
| `valence`         | Positiveness conveyed by the music.                                    |
| `tempo`           | Tempo in beats per minute (BPM).                                       |
| `duration_ms`     | Duration of the track in milliseconds.                                 |
| `time_signature`  | Estimated time signature (usually 3, 4, or 5).                         |
| `liked`           | User preference score (continuous numerical value).                    |

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## 🧼 Data Preprocessing

- ✅ Handled missing values
- ✅ Removed duplicates
- ✅ Normalized numeric features using **Z-score normalization**
- ✅ Detected outliers using box plots
- ✅ Computed summary statistics (mean, std, etc.)

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## 📊 Visual Analysis

Visualizations included in the analysis:

| Visualization                     | Description                                  |
|----------------------------------|----------------------------------------------|
| ![Missing Values](missing_values_graph.png)         | Heatmap of missing values                     |
| ![Unique and Duplicated](unique_duplicated.png)     | Count of unique and duplicate rows            |
| ![Z-Score](zscore.png)                             | Normalized feature distributions              |
| ![Top & Bottom Liked](top_bottom_liked.png)         | Most and least liked tracks                   |
| ![Distribution](distribution.png)                   | Histograms of key features                    |
| ![Danceability Histogram](danceability_histogram.png)| Focused view on `danceability` distribution   |
| ![Mean and STD](mean_std_plot.png)                  | Feature-wise mean and standard deviation      |
| ![Correlation Heatmap](correlation_heatmap.png)     | Feature correlation with `liked`              |
| ![Boxplot](boxplot.png)                             | Outlier detection via box plots               |

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## 📈 Key Findings

- 🔹 Features like `energy`, `valence`, and `danceability` have **positive correlation** with the `liked` score.
- 🔹 Features like `key` and `mode` show **low or no correlation** with user preference.
- 🔹 Most features are approximately **normally distributed**.
- 🔹 Tracks that are **highly energetic, positive, and danceable** are generally more liked.

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## 🛠️ Tools & Libraries Used

- **Language**: Python 🐍
- **Libraries**:
  - `pandas`, `numpy` for data handling
  - `matplotlib`, `seaborn` for visualizations
  - `scikit-learn` for preprocessing

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## 📌 Conclusion

This analysis helps identify which audio features most impact user preferences. These insights can guide the development of:
- 🎧 Music recommendation systems
- 📊 User behavior models
- 🤖 Feature engineering in ML projects

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## 👤 Author

**Sujal Thakkar**

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