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https://github.com/vidhi1290/lung-cancer-detection-app
🔍 Discover the future of healthcare with our Lung Cancer Detection Project. Using advanced machine learning techniques, we've achieved 92% accuracy in identifying lung cancer. Join us at the forefront of medical AI. 👩⚕️🌟 #AIHealthcare #LungCancerDetection
https://github.com/vidhi1290/lung-cancer-detection-app
artificial-intelligence cancer-detection datascience datascience-machinelearning kaggle lungcancer machine-learning machine-learning-algorithms numpy pandas python randomforest
Last synced: 21 days ago
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🔍 Discover the future of healthcare with our Lung Cancer Detection Project. Using advanced machine learning techniques, we've achieved 92% accuracy in identifying lung cancer. Join us at the forefront of medical AI. 👩⚕️🌟 #AIHealthcare #LungCancerDetection
- Host: GitHub
- URL: https://github.com/vidhi1290/lung-cancer-detection-app
- Owner: Vidhi1290
- Created: 2023-08-10T12:51:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-29T08:51:37.000Z (over 1 year ago)
- Last Synced: 2024-01-31T20:07:32.891Z (11 months ago)
- Topics: artificial-intelligence, cancer-detection, datascience, datascience-machinelearning, kaggle, lungcancer, machine-learning, machine-learning-algorithms, numpy, pandas, python, randomforest
- Language: Python
- Homepage:
- Size: 72.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
🔬 **Lung Cancer Detection Project**
📅 **First Commit:** 3 weeks ago
📁 **Repository Structure:**
- `app.py`: Initial commit for the application script.
- `label_encoder_areaq.pkl` and `label_encoder_smokes.pkl`: Label encoder files for categorical variables.
- `lung-cancer-detection-92-accuracy.ipynb`: Kaggle Notebook showcasing 92% accuracy lung cancer detection.
- `lung_cancer_model.pkl`: Trained Random Forest model.
📋 **Description:**
Welcome to the Lung Cancer Detection Project repository! 🩺🦠 In this project, we explore the world of machine learning and medical diagnostics. Our goal is to detect lung cancer with high accuracy using data-driven techniques.
🔍 **Project Highlights:**
- **Dataset:** We utilize the Kaggle lung cancer dataset, loaded using pandas.
- **Data Preprocessing:** Missing values are handled, and categorical variables are label-encoded for modeling.
- **Modeling:** A Random Forest Classifier is trained on the data to achieve an accuracy of 92%.
- **Saving Models:** Trained model and label encoders are saved using joblib.
- **Evaluation:** The model's predictions are evaluated, achieving a solid accuracy score.
📊 **Code Snippet:**
```python
# Loading the dataset
data = pd.read_csv('/kaggle/input/lung-cancer-dataset/lung_cancer_examples.csv')
# Handling missing values
data.dropna(inplace=True)
# Encoding the categorical variables
label_encoder_smokes = LabelEncoder()
label_encoder_areaq = LabelEncoder()
data['Smokes'] = label_encoder_smokes.fit_transform(data['Smokes'])
data['AreaQ'] = label_encoder_areaq.fit_transform(data['AreaQ'])
# Splitting the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initializing and training the Random Forest Classifier
rf_classifier = RandomForestClassifier(random_state=42)
rf_classifier.fit(X_train, y_train)
# Making predictions and evaluating the model
y_pred = rf_classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}') ```python
🚀 Join us on this journey to enhance medical diagnostics using machine learning. Feel free to explore our code, contribute, and provide feedback! 🤝👩💻👨💻