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02-Logistic-Regression-Project-Exercise

Updated
17 Sept 2026
4 min read
8 sections
Page 68 of 117
Python & Machine Learning

Logistic Regression Project Exercise

GOAL: Create a Classification Model that can predict whether or not a person has presence of heart disease based on physical features of that person (age,sex, cholesterol, etc...)

Complete the TASKs written in bold below.

Imports

TASK: Run the cell below to import the necessary libraries.

Data

This database contains 14 physical attributes based on physical testing of a patient. Blood samples are taken and the patient also conducts a brief exercise test. The "goal" field refers to the presence of heart disease in the patient. It is integer (0 for no presence, 1 for presence). In general, to confirm 100% if a patient has heart disease can be quite an invasive process, so if we can create a model that accurately predicts the likelihood of heart disease, we can help avoid expensive and invasive procedures.

Content

Attribute Information:

  • age
  • sex
  • chest pain type (4 values)
  • resting blood pressure
  • serum cholestoral in mg/dl
  • fasting blood sugar > 120 mg/dl
  • resting electrocardiographic results (values 0,1,2)
  • maximum heart rate achieved
  • exercise induced angina
  • oldpeak = ST depression induced by exercise relative to rest
  • the slope of the peak exercise ST segment
  • number of major vessels (0-3) colored by flourosopy
  • thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
  • target:0 for no presence of heart disease, 1 for presence of heart disease

Original Source: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Creators:

Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

TASK: Run the cell below to read in the data.

python
df = pd.read_csv('../DATA/heart.csv')
python
df.head()
Out · table
age
sex
cp
trestbps
chol
84 more lines
fbs
restecg
thalach
exang
oldpeak
slope
ca
thal
target
0
63
1
3
145
233
1
0
150
0
2.3
0
0
1
1
1
37
1
2
130
250
0
1
187
0
3.5
0
0
2
1
2
41
0
1
130
204
0
0
172
0
1.4
2
0
2
1
3
56
1
1
120
236
0
1
178
0
0.8
2
0
2
1
4
57
0
0
120
354
0
1
163
1
0.6
2
0
2
1
python
df['target'].unique()
Out · result
array([1, 0], dtype=int64)

Exploratory Data Analysis and Visualization

Feel free to explore the data further on your own.

TASK: Explore if the dataset has any missing data points and create a statistical summary of the numerical features as shown below.

python
# CODE HERE
python
# CODE HERE

Visualization Tasks

TASK: Create a bar plot that shows the total counts per target value.

python
# CODE HERE!

TASK: Create a pairplot that displays the relationships between the following columns:

['age','trestbps', 'chol','thalach','target']

Note: Running a pairplot on everything can take a very long time due to the number of features

python
# CODE HERE

TASK: Create a heatmap that displays the correlation between all the columns.

python
# CODE HERE

Machine Learning

Train | Test Split and Scaling

TASK: Separate the features from the labels into 2 objects, X and y.

python
# CODE HERE

TASK: Perform a train test split on the data, with the test size of 10% and a random_state of 101.

python
# CODE HERE

TASK: Create a StandardScaler object and normalize the X train and test set feature data. Make sure you only fit to the training data to avoid data leakage (data knowledge leaking from the test set).

python
# CODE HERE

Logistic Regression Model

*TASK: Create a Logistic Regression model and use Cross-Validation to find a well-performing C value for the hyper-parameter search. You have two options here, use LogisticRegressionCV OR use a combination of LogisticRegression and GridSearchCV. The choice is up to you.*

python
# CODE HERE
python
# help(LogisticRegressionCV)

TASK: Report back your search's optimal parameters, specifically the C value.

Note: You may get a different value than what is shown here depending on how you conducted your search.

python
# CODE HERE

Coeffecients

TASK: Report back the model's coefficients.

BONUS TASK: We didn't show this in the lecture notebooks, but you have the skills to do this! Create a visualization of the coefficients by using a barplot of their values. Even more bonus points if you can figure out how to sort the plot! If you get stuck on this, feel free to quickly view the solutions notebook for hints, there are many ways to do this, the solutions use a combination of pandas and seaborn.

python
#CODE HERE

Model Performance Evaluation

TASK: Let's now evaluate your model on the remaining 10% of the data, the test set.

TASK: Create the following evaluations:

  • Confusion Matrix Array
  • Confusion Matrix Plot
  • Classification Report
python
# CODE HERE
python
# CODE HERE
python
# CODE HERE

Performance Curves

TASK: Create both the precision recall curve and the ROC Curve.

python
# CODE HERE
python
# CODE HERE

Final Task: A patient with the following features has come into the medical office:

age 48.0 sex 0.0 cp 2.0 trestbps 130.0 chol 275.0 fbs 0.0 restecg 1.0 thalach 139.0 exang 0.0 oldpeak 0.2 slope 2.0 ca 0.0 thal 2.0

TASK: What does your model predict for this patient? Do they have heart disease? How "sure" is your model of this prediction?

For convience, we created an array of the features for the patient above

python
patient = [[ 54. ,   1. ,   0. , 122. , 286. ,   0. ,   0. , 116. ,   1. ,
          3.2,   1. ,   2. ,   2. ]]
python
# CODE HERE
python
# EXPECTED PREDICTION
Out · result
array([0], dtype=int64)
python
# EXPECTED PROBABILITY PER CLASS (Basically model should be extremely sure its in the 0 class)
Out · result
array([[9.99999862e-01, 1.38455917e-07]])

Great Job!