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03-Logistic-Regression-Project-Exercise-Solution

Updated
17 Sept 2026
4 min read
8 sections
Page 69 of 117
Python & Machine Learning

Logistic Regression Project Exercise - Solutions

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
df.info()
Out · stdout
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----
16 more lines
 0   age       303 non-null    int64  
 1   sex       303 non-null    int64  
 2   cp        303 non-null    int64  
 3   trestbps  303 non-null    int64  
 4   chol      303 non-null    int64  
 5   fbs       303 non-null    int64  
 6   restecg   303 non-null    int64  
 7   thalach   303 non-null    int64  
 8   exang     303 non-null    int64  
 9   oldpeak   303 non-null    float64
 10  slope     303 non-null    int64  
 11  ca        303 non-null    int64  
 12  thal      303 non-null    int64  
 13  target    303 non-null    int64  
dtypes: float64(1), int64(13)
memory usage: 33.3 KB
python
# CODE HERE
python
df.describe().transpose()
Out · table
count
mean
std
min
25%
129 more lines
50%
75%
max
age
303.0
54.366337
9.082101
29.0
47.5
55.0
61.0
77.0
sex
303.0
0.683168
0.466011
0.0
0.0
1.0
1.0
1.0
cp
303.0
0.966997
1.032052
0.0
0.0
1.0
2.0
3.0
trestbps
303.0
131.623762
17.538143
94.0
120.0
130.0
140.0
200.0
chol
303.0
246.264026
51.830751
126.0
211.0
240.0
274.5
564.0
fbs
303.0
0.148515
0.356198
0.0
0.0
0.0
0.0
1.0
restecg
303.0
0.528053
0.525860
0.0
0.0
1.0
1.0
2.0
thalach
303.0
149.646865
22.905161
71.0
133.5
153.0
166.0
202.0
exang
303.0
0.326733
0.469794
0.0
0.0
0.0
1.0
1.0
oldpeak
303.0
1.039604
1.161075
0.0
0.0
0.8
1.6
6.2
slope
303.0
1.399340
0.616226
0.0
1.0
1.0
2.0
2.0
ca
303.0
0.729373
1.022606
0.0
0.0
0.0
1.0
4.0
thal
303.0
2.313531
0.612277
0.0
2.0
2.0
3.0
3.0
target
303.0
0.544554
0.498835
0.0
0.0
1.0
1.0
1.0

Visualization Tasks

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

python
# CODE HERE!
python
sns.countplot(x='target',data=df)
Out · result
<AxesSubplot:xlabel='target', ylabel='count'>
Out · plot
Output 1

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
python
df.columns
Out · result
Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],
      dtype='object')
python
# Running pairplot on everything will take a very long time to render!
sns.pairplot(df[['age','trestbps', 'chol','thalach','target']],hue='target')
Out · result
<seaborn.axisgrid.PairGrid at 0x2573c4e2148>
Out · plot
Output 2

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

python
# CODE HERE
python
plt.figure(figsize=(12,8))
sns.heatmap(df.corr(),cmap='viridis',annot=True)
Out · result
<AxesSubplot:>
Out · plot
Output 3

Machine Learning

Train | Test Split and Scaling

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

python
# CODE HERE
python
X = df.drop('target',axis=1)
y = df['target']

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

python
# CODE HERE
python
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)

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
python
scaler = StandardScaler()
python
scaled_X_train = scaler.fit_transform(X_train)
scaled_X_test = scaler.transform(X_test)

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, the solutions use the simpler LogisticRegressionCV approach.*

python
# CODE HERE
python
from sklearn.linear_model import LogisticRegressionCV
python
# help(LogisticRegressionCV)
python
log_model = LogisticRegressionCV()
python
log_model.fit(scaled_X_train,y_train)
Out · result
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
python
log_model.C_
Out · result
array([0.04641589])
python
log_model.get_params()
Out · result
{'Cs': 10,
 'class_weight': None,
 'cv': None,
 'dual': False,
 'fit_intercept': True,
12 more lines
 'intercept_scaling': 1.0,
 'l1_ratios': None,
 'max_iter': 100,
 'multi_class': 'auto',
 'n_jobs': None,
 'penalty': 'l2',
 'random_state': None,
 'refit': True,
 'scoring': None,
 'solver': 'lbfgs',
 'tol': 0.0001,
 'verbose': 0}

Coeffecients

TASK: Report back the model's coefficients.

python
log_model.coef_
Out · result
array([[-0.09621199, -0.39460154,  0.53534731, -0.13850191, -0.08830462,
         0.02487341,  0.08083826,  0.29914053, -0.33438151, -0.352386  ,
         0.25101033, -0.49735752, -0.37448551]])

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
python
coefs = pd.Series(index=X.columns,data=log_model.coef_[0])
python
coefs = coefs.sort_values()
python
plt.figure(figsize=(10,6))
sns.barplot(x=coefs.index,y=coefs.values);
Out · plot
Output 4

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
from sklearn.metrics import confusion_matrix,classification_report,plot_confusion_matrix
python
y_pred = log_model.predict(scaled_X_test)
python
confusion_matrix(y_test,y_pred)
Out · result
array([[12,  3],
       [ 2, 14]], dtype=int64)
python
# CODE HERE
python
plot_confusion_matrix(log_model,scaled_X_test,y_test)
Out · result
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2573dba6e08>
Out · plot
Output 5
python
# CODE HERE
python
print(classification_report(y_test,y_pred))
Out · stdout
              precision    recall  f1-score   support

           0       0.86      0.80      0.83        15
           1       0.82      0.88      0.85        16
3 more lines
    accuracy                           0.84        31
   macro avg       0.84      0.84      0.84        31
weighted avg       0.84      0.84      0.84        31

Performance Curves

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

python
from sklearn.metrics import plot_precision_recall_curve,plot_roc_curve
python
# CODE HERE
python
plot_precision_recall_curve(log_model,scaled_X_test,y_test)
Out · result
<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x2573dc46cc8>
Out · plot
Output 6
python
# CODE HERE
python
plot_roc_curve(log_model,scaled_X_test,y_test)
Out · result
<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x2573dc484c8>
Out · plot
Output 7

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
X_test.iloc[-1]
Out · result
age          54.0
sex           1.0
cp            0.0
trestbps    122.0
chol        286.0
9 more lines
fbs           0.0
restecg       0.0
thalach     116.0
exang         1.0
oldpeak       3.2
slope         1.0
ca            2.0
thal          2.0
Name: 268, dtype: float64
python
y_test.iloc[-1]
Out · result
0
python
log_model.predict(patient)
Out · result
array([0], dtype=int64)
python
log_model.predict_proba(patient)
Out · result
array([[9.99999862e-01, 1.38455917e-07]])

Great Job!