___

Copyright by Pierian Data Inc. For more information, visit us at www.pieriandata.com

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.

In [1]:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

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.

In [2]:

df = pd.read_csv('../DATA/heart.csv')

In [3]:

df.head()

Out[3]:

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

In [4]:

df['target'].unique()

Out[4]:

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.

In [5]:

# CODE HERE

In [6]:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 303 entries, 0 to 302
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 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

In [7]:

# CODE HERE

In [8]:

Out[8]:

	count	mean	std	min	25%	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.

In [9]:

# CODE HERE!

In [10]:

Out[10]:

<AxesSubplot:xlabel='target', ylabel='count'>

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

In [11]:

# CODE HERE

In [13]:

Out[13]:

<seaborn.axisgrid.PairGrid at 0x2573c4e2148>

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

In [14]:

# CODE HERE

In [15]:

Out[15]:

<AxesSubplot:>

Machine Learning¶

Train | Test Split and Scaling¶

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

In [16]:

# CODE HERE

In [17]:

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

In [18]:

# CODE HERE

In [19]:

In [20]:

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).

In [21]:

# CODE HERE

In [22]:

In [23]:

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.

In [24]:

# CODE HERE

In [25]:

In [26]:

# help(LogisticRegressionCV)

In [27]:

In [28]:

Out[28]:

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.

In [29]:

# CODE HERE

Coeffecients¶

TASK: Report back the model's coefficients.

In [32]:

Out[32]:

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.

In [33]:

#CODE HERE

In [34]:

In [35]:

In [36]:

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

In [53]:

# CODE HERE

In [56]:

Out[56]:

array([[12,  3],
       [ 2, 14]], dtype=int64)

In [57]:

# CODE HERE

In [58]:

Out[58]:

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2573dba6e08>

In [59]:

# CODE HERE

In [60]:

              precision    recall  f1-score   support

           0       0.86      0.80      0.83        15
           1       0.82      0.88      0.85        16

    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.

In [64]:

# CODE HERE

In [65]:

Out[65]:

<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x2573dc46cc8>

In [66]:

# CODE HERE

In [67]:

Out[67]:

<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x2573dc484c8>

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

In [68]:

patient = [[ 54. ,   1. ,   0. , 122. , 286. ,   0. ,   0. , 116. ,   1. ,
          3.2,   1. ,   2. ,   2. ]]

In [1]:

# CODE HERE

In [71]:

# EXPECTED PREDICTION

Out[71]:

array([0], dtype=int64)

In [72]:

# EXPECTED PROBABILITY PER CLASS (Basically model should be extremely sure its in the 0 class)

Out[72]:

array([[9.99999862e-01, 1.38455917e-07]])

389 KiB

Raw Blame History

Logistic Regression Project Exercise¶

Imports¶

Data¶

Exploratory Data Analysis and Visualization¶

Visualization Tasks¶

Machine Learning¶

Train | Test Split and Scaling¶

Logistic Regression Model¶

Coeffecients¶

Model Performance Evaluation¶

Performance Curves¶

Great Job!¶

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1

389 KiB Raw Blame History

Logistic Regression Project Exercise¶

Imports¶

Data¶

Exploratory Data Analysis and Visualization¶

Visualization Tasks¶

Machine Learning¶

Train | Test Split and Scaling¶

Logistic Regression Model¶

Coeffecients¶

Model Performance Evaluation¶

Performance Curves¶

Great Job!¶

389 KiB

Raw Blame History

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	2	1