README.md

🎵 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.

---

📁 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).

---

🧼 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.)

---

📊 Visual Analysis

Visualizations included in the analysis:

Visualization	Description
	Heatmap of missing values
	Count of unique and duplicate rows
	Normalized feature distributions
	Most and least liked tracks
	Histograms of key features
	Focused view on danceability distribution
	Feature-wise mean and standard deviation
	Feature correlation with liked
	Outlier detection via box plots

---

📈 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.

---

🛠️ Tools & Libraries Used

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

---

📌 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

---

👤 Author

Sujal Thakkar

---