Skip to content

This project predicts lung cancer risks using machine learning models like Random Forest, Logistic Regression, and SVM. It analyzes patient data with features such as age, smoking habits, and symptoms. Data preprocessing, visualization, and performance evaluation ensure accurate predictions for early diagnosis.

License

Notifications You must be signed in to change notification settings

Yanne0800/Lung_Cancer_Prediction

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Lung Cancer Prediction with Machine Learning

Welcome to the Lung Cancer Prediction repository! This project focuses on utilizing machine learning models such as Random Forest, Logistic Regression, and Support Vector Machine (SVM) to predict lung cancer risks. By analyzing patient data that includes features like age, smoking habits, and symptoms, we aim to provide accurate predictions for early diagnosis.

Project Overview 📊

The primary goal of this project is to leverage machine learning algorithms to predict the likelihood of an individual developing lung cancer. By examining key patient data points and employing data preprocessing, visualization techniques, and performance evaluation metrics, we can generate reliable predictions crucial for timely medical interventions.

Key Features 🔑

  • Data Science: Applying advanced data analysis techniques to extract meaningful insights.
  • Data Visualization: Representing data visually to aid in understanding and decision-making.
  • Feature Engineering: Enhancing the predictive power of machine learning models.
  • Healthcare AI: Using AI to improve healthcare outcomes through accurate predictions.
  • Machine Learning Models: Implementing Random Forest, Logistic Regression, and SVM for effective prediction.

Repository Topics 📋

  • aiinhealthcare
  • datascience
  • datavisualization
  • decisiontreeclassifier
  • featureengineering
  • healthcareai
  • knn
  • logisticregression
  • lungcancerprediction
  • machinelearning
  • medicalanalysis
  • naivebayesclassifier
  • python
  • randomforestclassifier
  • scikitlearn
  • svm-classifier

Get Started 🚀

To explore the project further, visit the Releases section. Download the necessary files and execute them to delve into the world of lung cancer prediction using machine learning models.

Stay Connected 👨‍💻

Follow along with the latest updates and advancements in the field of lung cancer prediction by visiting the provided link. Dive deep into the world of healthcare AI and data science to make a positive impact on early cancer diagnosis.

Contribute 🤝

If you are passionate about leveraging machine learning for healthcare purposes, feel free to contribute to this project. Your expertise and insights can significantly enhance the prediction accuracy and help save lives through early detection.

Remember, the key to successful lung cancer prediction lies in the precise analysis of patient data and the effective utilization of machine learning algorithms. Stay focused, stay committed, and together, we can make a real difference in the fight against lung cancer.

Let's predict, prevent, and prevail! 🦾


Disclaimer: This README.md content is for informational purposes only and does not provide medical advice.

About

This project predicts lung cancer risks using machine learning models like Random Forest, Logistic Regression, and SVM. It analyzes patient data with features such as age, smoking habits, and symptoms. Data preprocessing, visualization, and performance evaluation ensure accurate predictions for early diagnosis.

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Contributors 2

  •  
  •