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LungCancer-prediction

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Lung Cancer Prediction Web App


Description
This project is a web app that predicts the likelihood of lung cancer based on various risk factors.

## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Screenshots](#screenshots)
- [Technologies Used](#technologies-used)
- [Model Information](#model-information)
- [References](#references)

## Installation
1. Clone the repository.
2. Install the required dependencies using `pip install -r requirements.txt`.
3. Run the web app using `streamlit run lungcancerpred_webapp.py`.

## Usage
1. Open the web app in your browser.
2. Enter the patient's details in the input fields.
3. Click on the "Predict" button to get the lung cancer prediction.

## Screenshots
![Screenshot 1](screenshots/sreamlitss.png)

## Technologies Used
- Python
- Streamlit
- Pandas
- Scikit-learn

## Model Information
The lung cancer prediction model was trained on a dataset of patient records and achieved an accuracy of 90%.


## References
1. Dataset source: [https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link]

	a/README.md		b/README.md
1	Lung Cancer Prediction Web App	1	Lung Cancer Prediction Web App
2		2
3	![Lung Cancer Prediction](screenshots/Untitled.png)
4	Description	3	Description
5	This project is a web app that predicts the likelihood of lung cancer based on various risk factors.	4	This project is a web app that predicts the likelihood of lung cancer based on various risk factors.
6		5
7	## Table of Contents	6	## Table of Contents
8	- [Installation](#installation)	7	- [Installation](#installation)
9	- [Usage](#usage)	8	- [Usage](#usage)
10	- [Screenshots](#screenshots)	9	- [Screenshots](#screenshots)
11	- [Technologies Used](#technologies-used)	10	- [Technologies Used](#technologies-used)
12	- [Model Information](#model-information)	11	- [Model Information](#model-information)
13	- [References](#references)	12	- [References](#references)
14		13
15	## Installation	14	## Installation
16	1. Clone the repository.	15	1. Clone the repository.
17	2. Install the required dependencies using `pip install -r requirements.txt`.	16	2. Install the required dependencies using `pip install -r requirements.txt`.
18	3. Run the web app using `streamlit run lungcancerpred_webapp.py`.	17	3. Run the web app using `streamlit run lungcancerpred_webapp.py`.
19		18
20	## Usage	19	## Usage
21	1. Open the web app in your browser.	20	1. Open the web app in your browser.
22	2. Enter the patient's details in the input fields.	21	2. Enter the patient's details in the input fields.
23	3. Click on the "Predict" button to get the lung cancer prediction.	22	3. Click on the "Predict" button to get the lung cancer prediction.
24		23
25	## Screenshots	24	## Screenshots
26	![Screenshot 1](screenshots/sreamlitss.png)	25	![Screenshot 1](screenshots/sreamlitss.png)
27		26
28	## Technologies Used	27	## Technologies Used
29	- Python	28	- Python
30	- Streamlit	29	- Streamlit
31	- Pandas	30	- Pandas
32	- Scikit-learn	31	- Scikit-learn
33		32
34	## Model Information	33	## Model Information
35	The lung cancer prediction model was trained on a dataset of patient records and achieved an accuracy of 90%.	34	The lung cancer prediction model was trained on a dataset of patient records and achieved an accuracy of 90%.
36		35
37		36
38	## References	37	## References
39	1. Dataset source: [https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link]	38	1. Dataset source: [https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link]