There is a excel that names Health_Data.csv, two columns of it named cholesterol and BMICat.Using decision Tree in Jupyter notebook to build a predictive model to ascertain if a person with a cholesterol level of 3 is mostly overweight.

Sure, here are the steps to build a decision tree model in Jupyter Notebook to predict if a person with a cholesterol level of 3 is mostly overweight using the Health_Data.csv file:

1. Import necessary libraries:

import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import graphviz

2. Load the dataset:

data = pd.read_csv('Health_Data.csv')

3. Prepare the data for modeling:

# Select the columns of interest
df = data[['cholesterol', 'BMICat']]

# Convert categorical column to numeric
df['BMICat'] = pd.factorize(df['BMICat'])[0]

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df[['cholesterol']], df['BMICat'], test_size=0.2, random_state=42)

4. Train the decision tree model:

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

5. Make predictions on the test set:

y_pred = model.predict(X_test)

6. Evaluate the model's accuracy:

accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

7. Visualize the decision tree:

dot_data = export_graphviz(model, out_file=None, 
                           feature_names=['cholesterol'],  
                           class_names=['Normal Weight', 'Overweight'],  
                           filled=True, rounded=True,  
                           special_characters=True)  
graph = graphviz.Source(dot_data)  
graph

This will display a decision tree that shows the rules used by the model to predict if a person with a cholesterol level of 3 is mostly overweight.