--- a/README.md
+++ b/README.md
@@ -1,39 +1,38 @@
-Lung Cancer Prediction Web App
-
-![Lung Cancer Prediction](screenshots/Untitled.png)
-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]
+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]