🩺 Disease Prediction using Machine Learning

A machine learning classification project that uses Logistic Regression to classify breast cancer cases using the Breast Cancer Wisconsin dataset available through Scikit-learn.

📌 Project Overview

This project demonstrates a complete beginner-friendly machine learning workflow, including:

• Dataset loading and exploration
• Data preprocessing
• Train-test splitting
• Logistic Regression model training
• Model evaluation
• Confusion matrix analysis
• ROC-AUC evaluation

The project was originally developed as part of my CodeAlpha Machine Learning internship.

📂 Dataset

The project uses the built-in Breast Cancer Wisconsin Diagnostic dataset from Scikit-learn.

• Samples: 569
• Features: 30
• Target: Binary classification
• Missing values: None

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Jupyter Notebook / Google Colab

🤖 Machine Learning Model

Logistic Regression

The dataset was divided into:

• Training set: 455 samples
• Testing set: 114 samples

The Logistic Regression model was trained with max_iter=10000.

📊 Model Performance

The model achieved:

Metric	Result
Accuracy	95.61%
ROC-AUC	0.9977

Classification Report

Class	Precision	Recall	F1-Score
0	0.97	0.91	0.94
1	0.95	0.99	0.97

Confusion Matrix

[[39  4]
 [ 1 70]]

These results are from the notebook run performed locally using the project environment.

🔄 Project Workflow

Load Dataset
     ↓
Explore Dataset
     ↓
Check Missing Values
     ↓
Prepare Features & Target
     ↓
Train-Test Split
     ↓
Train Logistic Regression
     ↓
Generate Predictions
     ↓
Evaluate Model
     ↓
Accuracy + Classification Report
     ↓
Confusion Matrix + ROC-AUC

📁 Project Files

• disease_prediction_code_alpha.ipynb — Complete Jupyter Notebook
• README.md — Project documentation
• .gitignore — Git ignored files and environment configuration

▶️ How to Run

1. Clone the repository

git clone https://github.com/AqsaBatool256/CodeAlpha_DiseasePrediction.git

2. Open the project

cd CodeAlpha_DiseasePrediction

3. Install the required libraries

pip install pandas numpy scikit-learn matplotlib seaborn jupyter

4. Open the notebook

jupyter notebook disease_prediction_code_alpha.ipynb

You can also open the notebook directly using VS Code with the Jupyter extension.

🎯 Learning Outcomes

Through this project, I practiced:

• Data exploration with Pandas
• Numerical data processing with NumPy
• Machine learning classification
• Logistic Regression
• Model evaluation
• Classification metrics
• Confusion matrix interpretation
• ROC-AUC analysis
• Working with Jupyter Notebooks
• Managing projects with Git and GitHub

👩‍💻 Author

Aqsa Batool Saqib

BS Computer Science Student | AI & Machine Learning Enthusiast | Python Developer | Cloud Computing Learner

• 💼 LinkedIn
• 🌐 Portfolio
• 💻 GitHub

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⭐ If you find this project useful, feel free to explore the repository and connect with me.