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# Lung Cancer Prediction

This repository contains a lung cancer prediction system that achieves an impressive accuracy of 99.67% using machine learning techniques. The system uses a deep learning model to predict the likelihood of lung cancer based on various input features such as age, gender, lifestyle factors, and medical history.








## Usage
1. Clone this repository to your local machine.
2. Install the required dependencies:
```
pip install pandas matplotlib Flask scikit-learn keras
```
3. Train the model by running:
```
python model.py
```
4. Start the Flask web application with:
```
python gui.py
```
7. Access the application through your web browser at http://localhost:5000.
8. Fill in the form with the required information.
9. Click the "Submit" button to get the predicted outcome.

## License
This project is licensed under the MIT License. See the LICENSE file for details.

With this Lung Cancer Prediction system, early detection of lung cancer becomes more accessible, potentially saving lives through timely intervention. Please note that this prediction system is not a substitute for professional medical diagnosis, and anyone with suspected lung cancer should seek advice from a qualified healthcare professional.

	a/README.md		b/README.md
1	# Lung Cancer Prediction	1	# Lung Cancer Prediction
2		2
3	This repository contains a lung cancer prediction system that achieves an impressive accuracy of 99.67% using machine learning techniques. The system uses a deep learning model to predict the likelihood of lung cancer based on various input features such as age, gender, lifestyle factors, and medical history.	3	This repository contains a lung cancer prediction system that achieves an impressive accuracy of 99.67% using machine learning techniques. The system uses a deep learning model to predict the likelihood of lung cancer based on various input features such as age, gender, lifestyle factors, and medical history.
4		4
5	## Screenshots	5
6	<p align="center">	6
7	<img src="/screenshots/home.JPG" alt="Home">
8	<img src="/screenshots/form.JPG" alt="Form">
9	<img src="/screenshots/results.JPG" alt="Results">
10	</p>
11
12	## Usage	7	## Usage
13	1. Clone this repository to your local machine.	8	1. Clone this repository to your local machine.
14	2. Install the required dependencies:	9	2. Install the required dependencies:
15	```	10	```
16	pip install pandas matplotlib Flask scikit-learn keras	11	pip install pandas matplotlib Flask scikit-learn keras
17	```	12	```
18	3. Train the model by running:	13	3. Train the model by running:
19	```	14	```
20	python model.py	15	python model.py
21	```	16	```
22	4. Start the Flask web application with:	17	4. Start the Flask web application with:
23	```	18	```
24	python gui.py	19	python gui.py
25	```	20	```
26	7. Access the application through your web browser at http://localhost:5000.	21	7. Access the application through your web browser at http://localhost:5000.
27	8. Fill in the form with the required information.	22	8. Fill in the form with the required information.
28	9. Click the "Submit" button to get the predicted outcome.	23	9. Click the "Submit" button to get the predicted outcome.
29		24
30	## License	25	## License
31	This project is licensed under the MIT License. See the LICENSE file for details.	26	This project is licensed under the MIT License. See the LICENSE file for details.
32		27
33	With this Lung Cancer Prediction system, early detection of lung cancer becomes more accessible, potentially saving lives through timely intervention. Please note that this prediction system is not a substitute for professional medical diagnosis, and anyone with suspected lung cancer should seek advice from a qualified healthcare professional.	28	With this Lung Cancer Prediction system, early detection of lung cancer becomes more accessible, potentially saving lives through timely intervention. Please note that this prediction system is not a substitute for professional medical diagnosis, and anyone with suspected lung cancer should seek advice from a qualified healthcare professional.