1. Music Data Analysis
2. Models That Used
3. Things weren't adding any value
4. Why Is The Acuuracy around 60%?
5. Deployment Step
6. Lastly

A Plots About The Class[The Target] To Get Insights

2.1- Random Forest 👉🏻 The best algorithm

Here is an evaluation about accuracy, performance & feature importance of Random Forest

2.2- LGBM

Here is an evaluation about accuracy, performance & feature importance of LightGBM

I have tried lots of models, tools, and thinking outside the box to enhance the performance and ⬆ the accuracy.

Some examples include:

• Deeplearning Models like DatRetClassifier
• Using Optuna to optimize the parameters
• Implementing normalization methods
• Employing an oversampling strategy

The accuracy is still around 50-60% due to the following reasons:

• I have dropped the categorical attribute instead of using label encoder
• There is no correlation between data points and the class
• Imbalanced classes, and so on

A snippet pictures about the app 👇🏻

Give a try to my app 👉🏻

Watch the app life on my youtube channel 👉🏻 https://youtu.be/LU80ixSVQ-c?si=koPdALvQjMxaznpg