🎵 Genre Classification using PCA and Logistic Regression

Tagline: Machine learning model for predicting music genres using PCA for dimensionality reduction and Logistic Regression for classification.

GitHub About Section (suggestion):
Music genre classification using feature-based data. Applies Principal Component Analysis (PCA) to reduce dimensionality and uses Logistic Regression to classify songs into genres efficiently and accurately.

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🚀 Overview

This project focuses on classifying music genres based on extracted audio features using a machine learning pipeline that combines Principal Component Analysis (PCA) and Logistic Regression.

The dataset includes musical attributes such as tempo, beats, loudness, duration, and spectral characteristics. The goal is to predict the genre of a track from its numerical features while improving training efficiency and avoiding overfitting through PCA.

This project demonstrates fundamental data science practices — data preprocessing, dimensionality reduction, classification, and model evaluation.

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📂 Repository Structure

GenreClassification_with_PCA_LogisticRegression/
│
├── classification.ipynb           # Main Jupyter notebook with the complete workflow
├── music_dataset_mod.csv          # Dataset containing music features and genre labels
├── Music Data Legend.xlsx         # Data dictionary explaining the dataset features
├── README.md                      # Project documentation (this file)
└── .gitignore                     # Ignored system files

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🧩 Dataset Description

The dataset music_dataset_mod.csv contains numerical representations of audio features extracted from songs, along with their corresponding genres.
The Music Data Legend.xlsx provides the feature definitions, which may include:

Feature	Description
tempo	Beats per minute (BPM) of the track
beats	Estimated number of beats in the song
loudness	Overall loudness (dB)
duration	Track duration in seconds
spectral features	Frequency-domain features from audio processing
chroma features	Pitch and tone-related metrics
genre	Target variable (Pop, Rock, Jazz, Hip-Hop, etc.)

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⚙️ Project Workflow

1. Data Preprocessing

• Load dataset (music_dataset_mod.csv) using pandas.
• Handle missing or outlier values.
• Normalize numerical features using StandardScaler.
• Encode categorical labels (genres) with LabelEncoder.

2. Principal Component Analysis (PCA)

• Apply PCA to reduce the high-dimensional feature space.
• Select optimal number of components based on explained variance (e.g., 95%).
• Visualize cumulative variance to justify dimensionality choice.

3. Model Training (Logistic Regression)

• Split dataset into training and testing sets (e.g., 80/20).
• Train Logistic Regression on PCA-transformed data.
• Optionally use regularization (C parameter) for tuning.

from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# Standardize and apply PCA
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
pca = PCA(n_components=0.95)  # Keep 95% variance
X_pca = pca.fit_transform(X_scaled)

# Train Logistic Regression
model = LogisticRegression(max_iter=1000)
model.fit(X_pca_train, y_train)

4. Model Evaluation

Evaluate model performance using classification metrics:

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

Example visualization:

from sklearn.metrics import confusion_matrix, classification_report
import seaborn as sns
import matplotlib.pyplot as plt

y_pred = model.predict(X_pca_test)
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, cmap="Greens", fmt="d")
plt.title("Confusion Matrix")
plt.show()

print(classification_report(y_test, y_pred))

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📊 Sample Results (replace with your actual values)

Metric	Score
Accuracy	84.6%
Precision	83.1%
Recall	82.4%
F1-score	82.7%

🧠 Interpretation:
The PCA + Logistic Regression pipeline achieves robust performance while drastically reducing computational cost. The model generalizes well, showing PCA effectively preserves the variance needed for genre discrimination.

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📈 PCA Explained Variance Example

Principal Component	Explained Variance (%)
PC1	31.5
PC2	17.2
PC3	10.4
PC4	7.9
PC5	5.3
Cumulative (Top 10 PCs)	95.1%

💡 PCA reduces hundreds of raw audio features to a smaller set of meaningful components without significant loss of information.

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🧠 Insights

• PCA effectively reduces dimensionality and computational time.
• Logistic Regression is interpretable and performs well with reduced features.
• Feature scaling is crucial before PCA for correct variance distribution.
• PCA ensures generalization, especially when dealing with highly correlated features.

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💡 Future Improvements

• Compare Logistic Regression with SVM or Random Forest.
• Implement hyperparameter tuning using GridSearchCV.
• Deploy as a Streamlit app to classify genres interactively.
• Visualize PCA clusters in 2D for genre separability.
• Extend dataset with more genres and tracks.

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🧰 Tech Stack

Category	Tools Used
Language	Python
Libraries	pandas, numpy, scikit-learn, matplotlib, seaborn
Modeling Techniques	PCA, Logistic Regression
Environment	Jupyter Notebook

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📦 How to Run

1. Clone the repository:

   git clone https://github.com/Shubham91999/GenreClassification_with_PCA_LogisticRegression.git
   cd GenreClassification_with_PCA_LogisticRegression

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook classification.ipynb

4. Run all cells sequentially to reproduce results.

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🧑‍🚀 Author

Shubham Kulkarni
Machine Learning Engineer | Data Science & AI Enthusiast
🔗 LinkedIn • GitHub

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🪙 License

This project is released under the MIT License — you may use and modify it for educational or research purposes.

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