Exploratory data analysis (EDA):
EDA uses statistics and visualization to analyze and identify trends in data sets. The main purpose of EDA is to determine whether a predictive model is an effective analytical tool for our problem. EDA helps data scientists understand datasets beyond formal modeling and hypothesis-testing tasks. Exploratory data analysis is essential to any exploratory analysis to gain insight into the dataset.
EDA is done in a sparate file and the models were trained in a separate file. The picle file that was generated was used to make the web application in the link: https://github.com/Ishrak30/Prediticting-Heart-Disease-using-Machine-Learning-Web-Application-
EDA can also be found in the kaggle notebook: https://www.kaggle.com/code/ishrakhussain/explaining-correlation-between-features-using-eda
The models can also be found in the kaggle notebook: https://www.kaggle.com/code/ishrakhussain/ml-prediction-and-roc-curve-with-8-models