# Music Genre Classification

## Project Goal

Predict the genre of a music track using a Kaggle dataset and multiple machine learning models.

## Data Processing Pipeline

1. Data loading
2. Checking duplicates and missing values
3. Removing unnecessary columns
4. Encoding categorical features
5. Feature engineering
6. Exploratory Data Analysis (EDA)
7. Outlier detection and handling
8. PCA for dimensionality reduction
9. Train/Test split
10. Standardization (applied only on training data to avoid data leakage)

## Models Used

* Logistic Regression
* KNN (K-Nearest Neighbors)
* Decision Tree
* Random Forest
* Gradient Boosting
* SVM
* Naive Bayes
* AdaBoost
* ExtraTrees
* XGBoost
* Neural Network

## Hyperparameter Tuning

GridSearchCV:

* Logistic Regression
* KNN
* Decision Tree
* Random Forest
* Gradient Boosting
* SVM
* Naive Bayes
* AdaBoost
* ExtraTrees
* XGBoost

RandomizedSearchCV:

* Neural Network

## Main File
music.ipynb