cancer prediction

This project focuses on building a machine learning system for cancer prediction using medical data analysis.

The goal is to classify whether a tumor is benign or malignant based on several medical features and patient data.

Project workflow included:

- Data preprocessing and cleaning

- Handling missing values

- Feature selection and normalization

- Exploratory Data Analysis (EDA)

- Building and training machine learning models

- Evaluating model performance using classification metrics

Several machine learning models were tested, including:

- Logistic Regression

- Random Forest

- XGBoost

- Artificial Neural Networks (ANN)

The models were evaluated using:

- Accuracy

- Precision

- Recall

- F1-Score

- Confusion Matrix