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Breast Cancer Prediction
⭐️A ML based code that predicts breast cancer⭐️

Demo video link Other links

Hello everyone! Today, I'm excited to share with you our project on breast cancer prediction using machine learning. Breast cancer is one of the most prevalent types of cancer affecting women worldwide. Early detection plays a crucial role in improving survival rates and treatment outcomes. Our project aims to utilize machine learning techniques to predict breast cancer. By analyzing relevant data, we can develop models that assist in early detection and treatment planning.

Team Details

Team number : VH224

Name Email
Shalini M shalinim.cse2022@citchennai.net
Krithika R krithikasai2204@gmail.com
Siddharth S siddharths.cse2022@citchennai.net
Sunitha Raj R sunitharajr.cse2022@citchennai.net
Image 1 Image 2 Image 3 Image 3

Problem statement

Develop machine learning models for accurate breast cancer prediction, aiding early detection and treatment planning. Utilize Anaconda Navigator and Jupyter Notebook, with TensorFlow, NumPy, Pandas, and Matplotlib, to preprocess data and train models. Aim to improve patient outcomes in breast cancer management.

About the project

We conducted our project in Anaconda Navigator, utilizing Jupyter Notebook for code development. We installed essential modules such as TensorFlow, NumPy, Pandas, and Matplotlib to support our machine learning implementation. After preprocessing the dataset, we developed a machine learning model to predict breast cancer cases. Through rigorous training and evaluation, we verified the effectiveness of our model in making accurate predictions. Our project contributes to the ongoing efforts in improving breast cancer diagnosis and treatment. By leveraging machine learning, we aim to enhance early detection, leading to better patient outcomes and healthcare management.

Technical implemntaion

  • Clean and normalize data using Pandas and Matplotlib for visualization.
  • Identify relevant features and visualize importance scores.
  • Utilize TensorFlow and traditional ML algorithms for model building.
  • Split data, train models, and evaluate performance using metrics.
  • Develop interface for seamless integration into healthcare systems.

flowchart )

Techstacks used

Anaconda Navigator , Jupyter Notebook , TensorFlow , NumPy , Pandas, Matplotlib

How to run locally

Step 1: Install Anaconda Navigator. Download and install Anaconda Navigator following OS-specific instructions.

Step 2: Launch Anaconda Navigator from your system.

Step 3: Create a new environment in the "Environments" section. Choose Python 3.8 or a compatible version.

Step 4: Install necessary packages by opening the environment with Jupyter Notebook or Spyder and navigating to the "Packages" tab.

Step 5: Launch Jupyter Notebook by clicking "Launch" in the "Home" tab.

Step 6: Upload project files directly into Jupyter Notebook.

Step 7: Open the desired code file within Jupyter Notebook.

Step 8: Run code cells sequentially to execute the code.

Step 9: Explore the generated results, visualizations, and output.

Step 10: Install any additional packages required using Anaconda Navigator.

Step 11: Close Jupyter Notebook when finished with the project.

What's next ?

  • Refine machine learning model for improved accuracy and efficiency in breast cancer prediction. Explore advanced algorithms and techniques to enhance performance.
  • Explore integration of deep learning or ensemble methods to enhance model robustness. Investigate AI-driven image analysis for breast cancer detection from medical images.
  • Engage with the community through workshops, seminars, and online resources to raise awareness about breast cancer prediction and early detection. Provide educational materials and tutorials to empower individuals to understand and utilize machine learning techniques for healthcare applications.
  • Stay updated on the latest advancements in machine learning and healthcare technology to continually improve the project. Conduct further research on breast cancer prediction and related fields to identify new opportunities for innovation.
  • Develop interactive visualization tools to provide users with a more intuitive understanding of the model's predictions and insights.

Declaration

We confirm that the project showcased here was either developed entirely during the hackathon or underwent significant updates within the hackathon timeframe. We understand that if any plagiarism from online sources is detected, our project will be disqualified, and our participation in the hackathon will be revoked.