Relevant in the age of online streaming, song recommendation systems provide customised playlists to increase consumer satisfaction. The objective of this work is to make a model using machine learning methods that will divide songs into “Hip-Hop” or “Rock” genres. Analyzing audio characteristics taken from The Echo Nest data collection will help one to accomplish this. We investigate several approaches on data preparation, feature selection, and machine learning algorithms using dancability, acoustic ness and other variables as measures. Therefore, it will enhance the efficiency of music recommendation systems by developing a model capable of efficiently categorizing songs with no human auditory evaluations.
Methodology
1. Collect and Preparing data
- Datasets: Despite other things in JSON format, it analyzes two main datasets including metadata regarding tracks in CSV format and thorough musical features such danceability and acoustic ness.
- Loading Data: The json and CSV files were imported to Python employing the json and pandas respectively.
- Data Merging: To create a combined dataset for study, the datasets were merged under a common identification.
2. Data cleaning
- missing values: either imputed values when appropriate or deleted records with significant missing data.
- Normalizing : the feature values will help to guarantee their consistent scale, so enhancing the performance of machine learning systems.
3. Exploratory Data Analysis (EDA)
- Using libraries including matplotlib and seaborn, exploratory data analysis (EDA)
- visualisation was used to graphically show the feature distribution and correlation with the target genres.
- Feature Importance: Found unique traits that set the Hip-Hop and Rock genres apart rather significantly.
4. Feature reduction
- Principal Component Analysis (PCA): In order to lower the dataset’s dimension count while still maintaining important information, so improving the model training process’s efficiency.
5. Model training
- Decision Trees, logistic regression, and k-Nearest Neighbours (k-NN) among other machine learning techniques were used to train several models.
- Hyperparameter Tuning: To improve the model’s performance by means of grid search and cross-valuation, hyperparameter optimisation
6. Model Assessment
- Measuring the models’ accuracy, precision, recall, and F1-score allowed one evaluate their effectiveness in classifying the different music genres.
- Examined confusion matrices to understand the particular kinds of classification errors the models produced.
7. Completion and Future work
- outcomes: Give a brief summary of the results, stressing the model with the best efficacy and the main elements that were very important in reaching correct classification.
- Future Improvements: Suggested improvements aim for using advanced algorithms, adding extra features, and extending the genre classification to incorporate a larger spectrum of categories.
This methodical approach ensures a comprehensive review of the task involving music classification and provides detailed evaluation that helps to enhance music recommendation systems
