This project develops a machine learning model to predict breast cancer based on various medical features. It demonstrates the application of data science techniques to a critical healthcare problem.

Key Features

• Utilizes a comprehensive dataset of breast cancer patients
• Implements multiple machine learning algorithms for comparison
• Performs detailed exploratory data analysis (EDA)
• Optimizes model performance through hyperparameter tuning
• Deploys the best-performing model for practical use

Project Structure

1. Data Preprocessing: Cleaning and preparing the dataset for analysis
2. Exploratory Data Analysis: Visualizing data to uncover patterns and relationships
3. Model Building: Training various algorithms including:
   • Logistic Regression
   • Decision Tree Classifier
   • Random Forest Classifier
   • Naive Bayes
   • K-Nearest Neighbors Classifier
4. Model Evaluation: Assessing performance using metrics like accuracy and F1 score
5. Hyperparameter Tuning: Optimizing models using Grid Search
6. Model Deployment: Exporting the best model for real-world application

Technologies Used

• Python
• NumPy and Pandas for data manipulation
• Seaborn, Plotly, and Matplotlib for data visualization
• Scikit-learn for machine learning algorithms
• Jupyter Notebook for development and documentation

Results

The final model achieves an impressive 91.8% accuracy in predicting breast cancer, demonstrating its potential for real-world medical applications.

View the Project

You can check out the full project notebook and code on my GitHub repository: Breast Cancer Prediction Model

Feel free to explore the code, run it yourself, or suggest improvements!

Dataset

The project uses a publicly available breast cancer dataset, which can be found here.

This project showcases skills in data analysis, machine learning, and practical application of AI in healthcare.