Stroke Prediction Web application using streamlt

Created a Web Application using Streamlit and Machine learning models on Stroke prediciton Whether the paitent gets a stroke or not on the basis of the feature columns given in the dataset

This Streamlit web app built on the Stroke Prediction dataset from Kaggle aims to provide a user-friendly interface for exploring and analyzing the dataset. Here's a summary of what the Streamlit web app might include:

1. Dataset Overview: The web app provides an overview of the Stroke Prediction dataset, including the number of records, features, and data types. It gives users a quick understanding of the dataset's structure.

2. Feature Selection: The web app allows users to select and analyze specific features from the dataset. They can choose variables of interest and explore their distribution, relationships with other variables, and their impact on stroke prediction.

3. Data Filtering: The web app enables users to filter the dataset based on specific criteria. They can apply filters such as age range, gender, or pre-existing conditions to examine subsets of the data and observe any trends or patterns.

4. Predictive Modeling: The web app can include machine learning models trained on the dataset for stroke prediction. Users can input their own data or modify existing data to obtain predictions and understand the factors influencing stroke risk.

5. Interactive Forms: The web app may provide interactive forms for users to input their own data or modify existing records. This allows them to experiment with different scenarios and observe the corresponding predictions or outcomes.

6. Model Performance Evaluation: The web app can present evaluation metrics for the predictive models, such as accuracy, precision, recall, or F1-score. Users can assess the performance of the models and gain insights into their reliability for stroke prediction.

7. Insights and Recommendations: The web app can provide insights derived from the dataset, such as identifying risk factors for stroke, highlighting correlations between variables, or offering recommendations for preventive measures. These insights can help users make informed decisions regarding stroke prevention.

Overall, the Streamlit web app on the Stroke Prediction dataset aims to provide an interactive and user-friendly platform for exploring and analyzing the data, making predictions, and gaining insights into stroke risk factors. It enables users to interact with the dataset and empowers them to make informed decisions regarding stroke prevention and management.