This project uses machine learning techniques to predict what type of music people are likely to enjoy based on their characteristics, such as age, gender, and other features. The goal is to build a predictive model that can assist in personalizing music recommendations.
- Data preprocessing: Handling missing values and encoding categorical data.
- Feature engineering: Selecting important features to improve model accuracy.
- Model training: Using machine learning algorithms to build a classifier.
- Evaluation: Measuring model performance with metrics like accuracy and F1-score.
Technologies Used
- Python
- Libraries:
- pandas
- NumPy
- scikit-learn
- Matplotlib/Seaborn (for data visualization)
Dataset The dataset contains the following columns:
- Age: The age of the person.
- Gender: The gender of the person.
- Favorite Genre: The music genre the person likes (e.g., Pop, Jazz, Rock).
- Listening Hours: The average number of hours spent listening to music daily.
- Other Features: Include additional characteristics related to music preferences.