Electronic Music Classification Project

Overview

This project focuses on classifying electronic music tracks using machine learning techniques. The data is sourced from BeatsDataset, and the model is built using popular Python libraries such as pandas, sklearn, matplotlib, and more.

You can find a detailed implementation of this project in my Kaggle notebook.

Project Workflow

1. Data Preprocessing:

   • The dataset is loaded using pandas and processed to clean and prepare the features for model training.
   • Categorical data is handled using the OneHotEncoder from sklearn.preprocessing and combined with numerical data using ColumnTransformer from sklearn.compose.

2. Data Splitting:

   • The dataset is split into training and testing sets using train_test_split from sklearn.model_selection.

3. Feature Scaling:

   • The features are scaled using the StandardScaler from sklearn.preprocessing to ensure that all features contribute equally to the model.

4. Model Training:

   • The classification model chosen for this task is the KNeighborsClassifier from sklearn.neighbors.
   • Model hyperparameters are tuned using cross-validation to achieve optimal performance.

5. Model Evaluation:

   • The model's performance is evaluated using accuracy metrics and visualized using matplotlib.pyplot.

Key Libraries Used

• pandas: For data manipulation and analysis.
• KNeighborsClassifier: A simple yet effective machine learning algorithm used for classification tasks.
• OneHotEncoder: For encoding categorical features.
• matplotlib.pyplot: For plotting and visualizing data and results.
• train_test_split: For splitting the dataset into training and testing subsets.
• ColumnTransformer: To apply different preprocessing steps to different columns.
• sklearn.preprocessing: Provides preprocessing utilities like scaling and encoding.
• sklearn.compose: Helps in combining multiple feature transformations into a single pipeline.

Dataset

The dataset used in this project is the BeatsDataset, which contains various features describing electronic music tracks.

References

• Kaggle Notebook: Electronic Music Classification
• BeatsDataset on Kaggle