This project involves predicting breast cancer diagnoses using machine learning models. The dataset includes various features related to cancer cell properties. The project utilizes Logistic Regression to classify whether a tumor is malignant or benign.
This project aims to classify breast cancer diagnoses using a dataset from the UCI Machine Learning Repository. The project involves Exploratory Data Analysis (EDA), feature scaling, and model evaluation to improve classification performance. Logistic Regression is applied to assess its effectiveness in predicting cancer diagnoses.
Link to Dataset: Breast Cancer Wisconsin Dataset
The dataset contains the following features:
- radius_mean: Mean of distances from center to points on the perimeter.
- texture_mean: Standard deviation of gray-scale values.
- smoothness_mean: Mean of local variation in radius lengths.
- compactness_mean: Mean of perimeter^2 / area - 1.0.
- concavity_mean: Mean of severity of concave portions of the contour.
- concave_points_mean: Mean for the number of concave portions of the contour.
- symmetry_mean: Mean of symmetry.
- fractal_dimension_mean: Mean of the fractal dimension.
- diagnosis: Diagnosis of breast cancer (M = malignant, B = benign).