In this dissertation work, I have implemented two different machine learning model namely; Support Vector Machine and k-Nearest Neighbour. In this particular file, i will try to split the steps in order to provide pathway to run each particular python notebook(pynb) file

Open the notebook feature_extraction.ipynb in Google Colab.

Upload or mount the Dataset: In the Colab notebook, upload the dataset directory from this link https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification/code or mount the dataset by using these two command lines: 1)from google.colab import drive 2)drive.mount('https://drive.google.com/drive/folders/1e2Q9rnlaqI7Wpyy01Ek_r-VVgZMRFRyC?usp=sharing')

Run the notebook cells to extract features and generate gtzan.csv, which i have already provided as an attachment. This gtzan file consists of 6 distinct features which i have had extracted for

Open the notebook SVM_Model.ipynb in Google Colab.

Prerequisites : Before running the feature extraction code, ensure that you have the following dependencies installed in your Google Colab environment: Librosa, NumPy, Pandas.

Again mount or upload the dataset and now place the gtzan.csv file in the same directory.

Run each of the cells to calculate different performance metrics.

Open the notebook kNN_Model.ipynb in Google Colab.

Prerequisites : Before running the feature extraction code, ensure to have installed the following dependencies in your Google Colab environment: Librosa, NumPy, Pandas.

Again mount or upload the dataset and now place the gtzan.csv file in the same directory.

Run each of the cells to calculate different performance metrics.

These are the respective outputs i got for my both models. I have attached the screenshot of the output for better understanding.

Svm model accuracy on test data : 74.5%

kNN model accuracy on test data : 67%