🧠 Breast Cancer Classification using Machine Learning

📌 Overview

This project builds a machine learning model to classify breast tumors as malignant or benign based on medical features.
It demonstrates the complete ML pipeline from data preprocessing to model evaluation.

---

🎯 Objective

To develop a reliable classification model that can assist in early detection of breast cancer.

---

⚙️ Tech Stack

• Python
• NumPy, Pandas
• Scikit-learn
• Matplotlib / Seaborn

---

📂 Dataset

• Breast Cancer dataset (from sklearn / UCI)
• Features include tumor radius, texture, perimeter, area, etc.

---

🔍 Workflow

1. Data Preprocessing

• Checked for missing values
• Feature scaling / normalization
• Train-test split

2. Model Building

• Logistic Regression
• Random Forest Classifier
• (Optional: Neural Network if used)

3. Model Evaluation

• Accuracy score
• Confusion Matrix
• Performance comparison

---

📊 Results

• Achieved accuracy: 85% (replace with your actual result)
• Model successfully classifies tumors with high reliability

---

💾 Model Saving

The trained model is saved using pickle as:

breast_cancer_model.pkl

This allows the model to be reused without retraining.