Classify_Songs_Genre

Load and Merge Data:

• Start by loading metadata about tracks and track metrics compiled by The Echo Nest using pandas. The data is merged based on the track_id and genre_top columns.

Pairwise Relationships and Correlation:

• Explore correlations and Visualize the correlation metrics using a heatmap.

Normalize Feature Data:

• Normalize the data using StandardScaler from scikit-learn to ensure fair treatment of different feature scales.

Principal Component Analysis (PCA):

• Apply PCA to reduce dimensionality. Explore scree plots to determine the number of components to use.

Further Visualize of PCA:

• Plot cumulative explained variance to determine the number of features required to explain a certain percentage of variance. In this case, aim for about 85% explained variance.

Train a Decision Tree:

• Utilize the lower-dimensional PCA projection to train a decision tree classifier. Split the data into training and testing sets and evaluate the model's performance.

Compare with Logistic Regression:

• Implement logistic regression as an alternative classification algorithm. Compare the performance of the decision tree and logistic regression using classification reports.

Cross-Validation for Evaluation:

• Apply k-fold cross-validation to get a more robust evaluation of model performance. Use both decision tree and logistic regression classifiers and examine the cross-validation scores.