🧬 Breast Cancer Classification – Model Building

A supervised machine learning project focused on implementing and comparing multiple classification algorithms using a real-world medical dataset.

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🚀 Run Notebook in Google Colab

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📘 Project Overview

• This project applies various classification algorithms to the Breast Cancer dataset available in the sklearn library.
• The dataset consists of 569 samples with 30 numerical features describing tumor characteristics, and a binary target variable indicating whether the tumor is Benign or Malignant.
• The primary goal is to build, evaluate, and compare multiple classification models to identify the most effective algorithm for this dataset.

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🎯 Objective

The project includes:

🔹 Dataset loading and preprocessing
🔹 Feature scaling and data preparation
🔹 Implementation of multiple classification algorithms
🔹 Model evaluation and comparison
🔹 Interpretation of results
🔹 Clean and reproducible Google Colab Notebook

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📂 Dataset Description

The dataset is sourced from sklearn.datasets.load_breast_cancer.

Component	Description
Samples	569
Features	30 numerical features
Target Classes	Benign (0), Malignant (1)

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🧹 Preprocessing Steps

✔ Dataset loading using load_breast_cancer()
✔ Converted into Pandas DataFrame
✔ Checked missing values (none found)
✔ Checked duplicates (none found)
✔ Train–Test split
✔ Feature scaling using StandardScaler

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🤖 Classification Algorithms Implemented

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine (SVM)
• k-Nearest Neighbors (k-NN)

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📊 Model Evaluation Metrics

• Accuracy Score
• Confusion Matrix
• Precision
• Recall
• F1-score

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📊 Model Performance Comparison

Model	Accuracy
Logistic Regression	0.982456
SVM (RBF)	0.982456
k-NN	0.956140
Random Forest	0.956140
Pruned Decision Tree	0.921053
Decision Tree	0.912281

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🏆 Best and Worst Performing Models

Best Model: Logistic Regression & SVM (RBF) → 0.982
Worst Model: Decision Tree → 0.912

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🧠 Key Observations

✔ Feature scaling improved SVM and k-NN performance
✔ Logistic Regression & SVM performed best
✔ Random Forest provided good generalization
✔ Decision Tree showed overfitting
✔ Pruning improved Decision Tree

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🛠 Tech Stack

Tool	Purpose
Python	Programming
Pandas	Data handling
NumPy	Computation
Matplotlib	Visualization
Seaborn	Visualization
Scikit-learn	ML models
Google Colab	Development

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📁 Repository Structure

Classification-Algorithms-Model-Building/

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├── Classification_Algorithms_Model_Building.ipynb

├── README.md

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🚀 How to Run the Project

1️⃣ Open the notebook using the Colab link above
2️⃣ Run all cells sequentially
3️⃣ View model results and evaluation

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📌 Academic Submission

This project was created as part of a Machine Learning academic assignment, demonstrating the implementation and comparison of multiple classification algorithms using a real-world medical dataset.

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⚠️ Limitations

• Small dataset size
• Limited features
• Risk of overfitting
• No extensive hyperparameter tuning
• Not tested on external datasets

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📌 Future Enhancements

• Apply GridSearchCV tuning
• Use advanced models (XGBoost, LightGBM)
• Add cross-validation
• Build Streamlit web app
• Improve visualization dashboard

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👤 Author

Name: Laya Mary Joy

Organization: Entri Elevate

Date: January 19, 2026

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⭐ Acknowledgment

Thanks to Entri Elevate for guidance and support throughout this project.

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