Breast Cancer Classification Project Using Machine Learning

I am pleased to share with you the Breast Cancer Classification Project using Machine Learning, where a set of models and techniques were used to analyze medical data and classify the tumor into malignant or benign based on a set of clinical characteristics. Models used: Several machine learning models were applied and compared to ensure the highest possible accuracy, including: Logistic Regression: A simple and fast model based on linearity in class separation. Support Vector Machine (SVC): Tested with multiple types of kernels (linear, RBF, and polynomial) to improve performance. Random Forest: Used to build multiple decision trees and combine their results to reduce errors. Gradual Boosting: A robust model that gradually improves performance. Kest Neighbors (KNN): A model based on the geographical proximity of samples to classify them. XGBoost: An advanced model that enhances performance quickly and accurately, especially with big data. Libraries used: Several libraries were relied upon to develop the project and achieve its goals, including: NumPy and Pandas: For data analysis and processing. Matplotlib and Seaborn: To create graphs and visualize data. Skit-learn: To apply models, measure data, and use grid search techniques such as GridSearchCV and RandomizedSearchCV. Imbalanced-learn: To handle data imbalance using SMOTE. XGBoost: To apply advanced boosting in classification. Data Analysis and Graphical Visualizations: Exploratory Data Analysis (EDA) was used to clarify important relationships and features. The analyses included: Distribution plotting using Seaborn to analyze the distribution of independent features. Correlation matrix to identify relationships between features