Academic / Machine Learning

Data Mining

Coursework projects for NTU's Data Mining and Machine Learning module, applying classification and optimization algorithms to real-world datasets in Python and Jupyter.

Project overview

This collection explores how data preparation, classification, and optimization can turn messy real-world datasets into useful decisions.

What I worked on

Prepared and explored real-world datasets in Jupyter notebooks.
Applied classification algorithms and compared model performance.
Used optimization techniques to improve the quality of results.

Implementation snippet

A look at the solution

from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X_train, X_test, y_train, y_test = train_test_split(
    features, labels, test_size=0.2, random_state=42
)

model.fit(X_train, y_train)
print(classification_report(y_test, model.predict(X_test)))