#Load your file (updated_data.csv )
#Variables Age Sex DM Hypertension Dyslipidemia Smoking CAD CRF BMI PreEF Vessles PostEF
#Run jupyter notebook
#Exploratory data analysis (EDA) the very first step in a data project.
#We will create a code-template to achieve this with one function.
#Step 1 – First approach to data
#Step 2 – Analyzing categorical variables
#Step 3 – Analyzing numerical variables
#Step 4 – Analyzing numerical and categorical at the same time
#Covering some key points in a basic EDA:
#Data types
#Outliers
#Missing values
#Distributions (numerically and graphically) for both, numerical and categorical variables.
#Linear Discriminant Analysis (LDA)
#Classification and Regression Trees (CART).
#k-Nearest Neighbors (kNN).
#Support Vector Machines (SVM) with a linear kernel.
#Random Forest (RF)