/End-to-end-Machine_Learning_projects

An end-to-end machine learning project for stroke prediction

Primary LanguageJupyter NotebookMIT LicenseMIT

End-to-end-Machine_Learning_projects

In this repository, I have been building an end-to-end machine learning project for stroke prediction with Streamlit, FastAPI.

The dataset of stroke prediction on Kaggle with 11 features and this project includes steps as below:

  1. Data loading
  2. Exploratory data analysis
  3. Data processing including filling null values, outliers handling, categorical data handling, checking correlations, handling imbalanced data and data scaling.
  4. Modelling and evaluation with Random Forrest, Grid Search and Cross Validation.
  5. Write and save model by using pickle
  6. Create an API with FastAPI.
  7. Create an UI with Streamlit.