• Stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. The given dataset can be used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, bmi value, various diseases, and smoking status.

• Scikit Learn: ML Library used
• TensorFlow Keras: ML Libraries used
• HTML: HTML documentation used
• Javscript: Javscript framework used
• Pandas: Python data manipulation libraries
• Seaborn: Data visualisation library

This is the main file with all the preprocessing, visualisations, various Machine learning and Deep Learning Models.

• Installing libraries and dependency
• Importing the dataset - Stroke Prediction Dataset
• Exploratory Data Analysis and Visualisation
• Data Preprocessing - Basic preprocessing and cleaning the dataset
• Dividing the dataset into train and test
• Applying Machine Learning models
• Applying Deep Learning models
  • Saving the weights and .json file for deployment

• Run the cells according to above mentioned pipeline