数据集获取

# 克隆仓库（含数据与代码）
git clone https://github.com/AhmedHosamMorgan/Supervised-Unsupervised-Machine-Learning-Models-on-Breast-Cancer-Dataset-Using-Ai..git

# 或仅下载打包文件
curl -L -o repo.zip https://github.com/AhmedHosamMorgan/Supervised-Unsupervised-Machine-Learning-Models-on-Breast-Cancer-Dataset-Using-Ai./archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

End of Semester Project For Machine Learning For Artificial intelligence Subject - Future University In Egypt (FUE)
Supervised/Unsupervised Machine Learning Models on “Breast Cancer” Dataset Using Ai.

Breast Cancer Diagnosis Prediction

This project aims to predict breast cancer diagnosis using machine learning techniques. It utilizes the Breast Cancer Wisconsin (Diagnostic) dataset, available in the data.csv file, containing various features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The target variable, ''diagnosis'', indicates whether the tumor is malignant (M) or benign (B).

Workflow Overview

1. Data Preprocessing:
   • Load the dataset using Pandas.
   • Separate features (X) and the target variable (y).
   • Encode the target variable into binary labels (Malignant: 1, Benign: 0).
   • Scale the features using StandardScaler to ensure uniformity in data distribution.
2. Model Training and Evaluation:
   • Employ Linear Regression for predicting the diagnosis.
   • Utilize k-fold cross-validation (k=3) to evaluate model performance.
   • Calculate evaluation metrics such as Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE) to assess the model’'s accuracy.
3. K-means Clustering:
   • Apply K-means clustering to identify patterns within the dataset.
   • Determine the optimal number of clusters using the Elbow Method.
   • Calculate the Sum of Squared Errors (SSE) and Silhouette Coefficient to evaluate clustering performance.
4. Visualization:
   • Plot the clusters obtained from K-means clustering, along with the centroids.

Instructions

To run the project:

1. Ensure you have Python installed on your system.

2. Clone this repository to your local machine:

   git clone https://github.com/username/breast-cancer-diagnosis-prediction.git
   

3. Install the required Python libraries using pip:

   pip install pandas scikit-learn matplotlib
   

4. Run the main.py script:

   python main.py
   

5. View the output, including model evaluation metrics and visualizations.

File Structure

• main.py: Entry point of the program. Contains the main workflow, including data preprocessing, model training, and visualization.

• data.csv: Dataset containing features and target variable.

Contributing

Contributions to this proje

数据说明

该数据集包含从乳腺肿块细针抽吸（FNA）的数字图像中计算出的各种特征，用于通过机器学习模型预测乳腺癌诊断，目标变量为肿瘤恶性（M）或良性（B）。

完整仓库：github.com/AhmedHosamMorgan/Supervised-Unsupervised-Machine-Learning-Models-on-Breast-Cancer-Dataset-Using-Ai.