https://github.com/yanne0800/lung_cancer_prediction
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.
https://github.com/yanne0800/lung_cancer_prediction
algorithm classification cnn decision-tree-classifier decision-trees deep-learning gradientboosting keras lung-cancer medical-image-processing navies-bayes-classifer neuralnetworks python scikit-learn
Last synced: 7 months ago
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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.
- Host: GitHub
- URL: https://github.com/yanne0800/lung_cancer_prediction
- Owner: Yanne0800
- License: mit
- Created: 2025-03-25T21:51:32.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-25T23:09:22.000Z (7 months ago)
- Last Synced: 2025-03-25T23:24:46.694Z (7 months ago)
- Topics: algorithm, classification, cnn, decision-tree-classifier, decision-trees, deep-learning, gradientboosting, keras, lung-cancer, medical-image-processing, navies-bayes-classifer, neuralnetworks, python, scikit-learn
- Language: Jupyter Notebook
- Size: 1.36 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://github.com/Yanne0800/Lung_Cancer_Prediction/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! 🦾
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*Disclaimer: This README.md content is for informational purposes only and does not provide medical advice.*