- 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