Breast Cancer Prediction Using Classification Algorithms

1. Introduction Breast cancer is one of the most prevalent forms of cancer worldwide, and early diagnosis significantly increases the chances of survival. Machine learning techniques can play a crucial role in predicting breast cancer by analyzing medical data. In this project, the Breast Cancer Wisconsin Dataset is used to classify tumors as malignant (cancerous) or benign (non-cancerous) using various classification algorithms that were covered during the course.
2. Objective  To apply and compare multiple machine learning classification algorithms on the Breast Cancer Wisconsin Dataset.  To evaluate their performance and identify the best algorithm for tumor classification.
3. Dataset Description  Name: Breast Cancer Wisconsin Dataset  Source: UCI Machine Learning Repository  Number of Instances: 569  Number of Features: 30 numerical features (e.g., mean radius, mean texture, etc.)  Target Variable: Binary classification - 0 (Benign) and 1 (Malignant).
4. Algorithms Used We will apply and compare the following classification algorithms:
5. Logistic Regression
6. Decision Tree Classifier
7. Random Forest Classifier
8. K-Nearest Neighbors (KNN)
9. Support Vector Machine (SVM)
10. Bagging Classifier