Preliminary analysis:

a$.$ Perform preliminary data inspection and report the findings on the structure of the data, missing

values, duplicates, etc.

b$.$ Based on these findings, remove duplicates $($if any$)$ and treat missing values using an appropriate

strategy

Prepare a report about the data explaining the

distribution of the disease and the related factors

using the steps listed below:

a$.$ Get a preliminary statistical summary of the data and explore the measures of central tendencies

and spread of the data

b$.$ Identify the data variables which are categorical and describe and explore these variables using

the appropriate tools, such as count plot

c$.$ Study the occurrence of CVD across the Age category

d$.$ Study the composition of all patients with respect to the Sex category

e$.$ Describe the relationship between cholesterol levels and a target variable

f$.$ Use a pair plot to understand the relationship between all the given variables

Build a baseline model to predict the risk of a heart

attack using a Decision Tree Classifier. Apply different

model assessment methods

Important Note:

$1-$ The dataset include null value$/$values$.$ In the cleaning phase of the data remove all

null values and$/$or duplicated values. To remove the null values use the code

below for example:

health$\_$care.dropna$($inplace$=$True$)$

This line will remove all rows with null values from the dataframe

$2-$ The code for decision Tree Classifier should be as follows: