Cancer Analysis (KMeans + Logistic Regression)

This project works with a cancer dataset and uses two machine learning methods:
KMeans is used to find groups in the data.
Logistic Regression is used to predict cancer type.

Project Goal

Understand the data using KMeans clustering.
Build a model to predict cancer type.
Check the model performance.

Dataset

The dataset is loaded from UCI using ucimlrepo.
Dataset ID: 17.
The data is converted into a Pandas DataFrame.
The features are numeric.

Data Preparation

Converted the dataset into a DataFrame.
Scaled the features using StandardScaler and MinMaxScaler.
Split the data into training and test sets.
Used PCA to reduce dimensions and visualize the data.

Models Used

KMeans (Unsupervised Learning)

Applied KMeans clustering.
Used inertia (elbow method) to understand clustering behavior.

Logistic Regression (Supervised Learning)

Trained a Logistic Regression model.
Evaluated the model using:

• Accuracy
• Recall
• F1-score
• Confusion Matrix
• Classification Report