Overview
This repository contains code and resources for detecting lung cancer using machine learning models. The dataset used for this project is stored in a CSV file.
Dataset
The dataset used for training and testing the models is stored in data.csv. It includes the following columns:
- GENDER
- AGE
- SMOKING
- YELLOW_FINGERS
- ANXIETY
- PEER_PRESSURE
- CHRONIC DISEASE
- FATIGUE
- ALLERGY
- WHEEZING
- ALCOHOL CONSUMING
- COUGHING
- SHORTNESS OF BREATH
- SWALLOWING DIFFICULTY
- CHEST PAIN
- LUNG_CANCER (Target: Indicates the presence or absence of lung cancer)
Machine Learning Models
The following machine learning models have been implemented and evaluated:
- Linear Regression
- Logistic Regression
- Gradient Boosting
- k-Nearest Neighbors (KNN)
- Decision Tree Classifier
- Random Forest Classifier
- CATBoost Classifier
- XGBoost Classifier
Usage
Environment Setup
- Clone this repository.
- Install the required dependencies using
pip install -r requirements.txt. - Ensure Python 3.x is used.
Running the Models
-
model.ipynbcontains the model training and testing process for all the models. -
Execute the file to train and evaluate the model.
-
NOTE: Use a separate conda environment for avoiding future errors with dependencies.
Results
The performance metrics and evaluation results for each model are documented in the respective model's notebook.
Future Improvements
- Deploying the Model into a Web Application with a proper UI.
- Feature engineering techniques to enhance model performance.
- Hyperparameter tuning for optimizing model accuracy.
- Incorporating deep learning models for comparison.
Contribution
Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.
License
This project is licensed under the Apache License.
Acknowledgments
- Dataset Credits : https://www.kaggle.com/datasets/mysarahmadbhat/lung-cancer
- Reference : https://www.kaggle.com/code/casper6290/lung-cancer-prediction-98
The above notebook was helpful in terms of understanding the preprocessing and model development process properly.