https://github.com/uni-creator/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/uni-creator/lung_cancer_prediction
aiinhealthcare datascience datavisualization decisiontreeclassifier featureengineering healthcareai knn logisticregression lungcancerprediction machinelearning medicalanalysis naivebayesclassifier python randomforestclassifier scikitlearn svm-classifier
Last synced: about 1 month ago
JSON representation
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/uni-creator/lung_cancer_prediction
- Owner: Uni-Creator
- Created: 2025-03-20T17:50:41.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-03-20T17:58:32.000Z (about 1 month ago)
- Last Synced: 2025-03-20T18:39:53.114Z (about 1 month ago)
- Topics: aiinhealthcare, datascience, datavisualization, decisiontreeclassifier, featureengineering, healthcareai, knn, logisticregression, lungcancerprediction, machinelearning, medicalanalysis, naivebayesclassifier, python, randomforestclassifier, scikitlearn, svm-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Lung Cancer Prediction Using Machine Learning
This project leverages various machine learning models to predict lung cancer based on patient data. It involves data preprocessing, model training, and performance evaluation using key metrics like accuracy, precision, recall, and F1-score.
## Dataset
The dataset contains 309 rows and 16 columns with the following features:
- **GENDER** (M/F)
- **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 Variable)
## Data Preprocessing
1. **Data Cleaning:**
- Missing values checked (no missing values found).
- Data types converted to integers for compatibility with machine learning models.
2. **Encoding Categorical Variables:**
- `GENDER` mapped to {M: 1, F: 2}.
- `LUNG_CANCER` mapped to {YES: 1, NO: 2}.
3. **Data Splitting:**
- 2/3 for training, 1/3 for testing using `train_test_split()`.
## Machine Learning Models
### 1. Logistic Regression
- **Accuracy:** 0.883
- **Precision:** 0.895
- **Recall:** 0.977
- **F1 Score:** 0.934
### 2. K-Nearest Neighbors (KNN)
- **Accuracy:** 0.874
- **Precision:** 0.878
- **Recall:** 0.989
- **F1 Score:** 0.930
### 3. Decision Tree
- **Accuracy:** 0.874
- **Precision:** 0.878
- **Recall:** 0.989
- **F1 Score:** 0.930
### 4. Support Vector Machine (SVM)
- **Accuracy:** 0.845
- **Precision:** 0.845
- **Recall:** 1.0
- **F1 Score:** 0.916
### 5. Naive Bayes
- **Accuracy:** 0.864
- **Precision:** 0.910
- **Recall:** 0.931
- **F1 Score:** 0.920
### 6. Random Forest
- **Accuracy:** 0.893
- **Precision:** 0.904
- **Recall:** 0.977
- **F1 Score:** 0.939
## Model Evaluation Metrics
- **Confusion Matrix:** Visualized using Seaborn heatmaps.
- **Precision, Recall, and F1-score:** Calculated to assess model performance in detail.
## Data Visualization
- **Correlation Heatmap:** Visualizes feature relationships.
- **Distribution Plots:** Histograms for each feature to understand data distribution.
## Libraries Used
- `pandas`
- `matplotlib`
- `seaborn`
- `scikit-learn`
## How to Run
1. Install dependencies:
```bash
pip install pandas matplotlib seaborn scikit-learn
```
2. Load the dataset: Ensure `survey_lung_cancer.csv` is in the working directory.
3. Run the Python script to execute the entire pipeline.
## Conclusion
This project effectively demonstrates lung cancer prediction using various machine learning models, with Random Forest achieving the highest performance.
- For inquiries, reach me at [[email protected]](mailto:[email protected])
- [LinkedIn](https://www.linkedin.com/in/abhay-singh312/) - Let's connect!
- [Github](https://github.com/Abhay-Singh312) - Check out my other works!