This lab focuses on preparing the Breast Cancer dataset for machine learning by improving its quality and structure. The dataset contains 30 numerical features extracted from cell nuclei images, which are used to classify tumors as malignant or benign. The goal is to identify issues in the data and apply preprocessing techniques to make it suitable for accurate analysis and model training.
- Identified data quality issues in the dataset
- Handled missing values using an appropriate strategy
- Detected and treated outliers to reduce their impact
- Applied normalization techniques to standardize numerical features
- Reduced data dimensionality using PCA after analyzing feature relationships