Exploratory data analysis

Importing libraries and packages

# Mathematical operations and data manipulation
import numpy as np
import pandas as pd

# Visualisation
import seaborn as sns
import matplotlib.pyplot as plt

# Warnings
import warnings

warnings.filterwarnings("ignore")

%matplotlib inline

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

# load data
dataset = pd.read_csv(f"{data_path}/preprocessed_heart.csv")
dataset.head().T

	0	1	2	3	4
age	63.0	37.0	41.0	56.0	57.0
sex	1.0	1.0	0.0	1.0	0.0
chest_pain	3.0	2.0	1.0	1.0	0.0
rest_bp	145.0	130.0	130.0	120.0	120.0
chol	233.0	250.0	204.0	236.0	354.0
fast_bld_sugar	1.0	0.0	0.0	0.0	0.0
rest_ecg	0.0	1.0	0.0	1.0	1.0
max_hr	150.0	187.0	172.0	178.0	163.0
ex_angina	0.0	0.0	0.0	0.0	1.0
st_depr	2.3	3.5	1.4	0.8	0.6
slope	0.0	0.0	2.0	2.0	2.0
colored_vessels	0.0	0.0	0.0	0.0	0.0
thalassemia	1.0	2.0	2.0	2.0	2.0
target	1.0	1.0	1.0	1.0	1.0

Plotting distributions and relationships

Plotting the Distributions and Relationships Between Specific Features

sns.set(
    palette="pastel",
    rc={
        "figure.figsize": (16, 10),
        "axes.titlesize": 18,
        "axes.labelsize": 16,
        "xtick.labelsize": 14,
        "ytick.labelsize": 16,
    },
)

g = sns.countplot(x="age", data=dataset)
g.set_title("Distribution of Age")
plt.xlabel("Age")

Text(0.5, 0, 'Age')

print(dataset["target"].value_counts())
print()
print(dataset["target"].value_counts(normalize=True))

1    165
0    138
Name: target, dtype: int64

1    0.544554
0    0.455446
Name: target, dtype: float64

a = sns.countplot(x="target", data=dataset)
a.set_title("Distribution of Presence of Heart Disease")
a.set_xticklabels(["Absent", "Present"])
plt.xlabel("Presence of Heart Disease")
plt.show()

print(dataset["sex"].value_counts())
print()
print(dataset["sex"].value_counts(normalize=True))

1    207
0     96
Name: sex, dtype: int64

1    0.683168
0    0.316832
Name: sex, dtype: float64

b = sns.countplot(x="target", data=dataset, hue="sex")
plt.legend(["Female", "Male"])
b.set_title("Distribution of Presence of Heart Disease by Sex")
b.set_xticklabels(["Absent", "Present"])
plt.show()

Plotting Distributions and Relationships between Columns with Respect to the Target Column

print(dataset["chest_pain"].value_counts())
print()
print(dataset["chest_pain"].value_counts(normalize=True))

0    143
2     87
1     50
3     23
Name: chest_pain, dtype: int64

0    0.471947
2    0.287129
1    0.165017
3    0.075908
Name: chest_pain, dtype: float64

c = sns.countplot(x="chest_pain", data=dataset, hue="target")
plt.legend(["Absent", "Present"])
c.set_title("Distribution of Presence of Heart Disease by Chest Pain Type")
c.set_xticklabels(
    ["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"]
)
plt.show()

print(dataset["colored_vessels"].value_counts())
print()
print(dataset["colored_vessels"].value_counts(normalize=True))

0    175
1     65
2     38
3     20
4      5
Name: colored_vessels, dtype: int64

0    0.577558
1    0.214521
2    0.125413
3    0.066007
4    0.016502
Name: colored_vessels, dtype: float64

d = sns.countplot(x="colored_vessels", data=dataset, hue="target")
plt.legend(["Absent", "Present"])
d.set_title(
    "Distribution of Presence of Heart Disease by Number of Major "
    "Vessels Coloured by Fluoroscopy"
)
plt.show()

print(dataset["slope"].value_counts())
print()
print(dataset["slope"].value_counts(normalize=True))

2    142
1    140
0     21
Name: slope, dtype: int64

2    0.468647
1    0.462046
0    0.069307
Name: slope, dtype: float64

f = sns.countplot(x="slope", data=dataset, hue="target")
plt.legend(["Absent", "Present"])
f.set_title("Distribution of Presence of Heart Disease by Slope")
f.set_xticklabels(["Upsloping", "Flat", "Downsloping"])
plt.show()

Plotting the Relationship between the Presence of Heart Disease and Maximum Recorded Heart Rate

# Plotting the Relationship between the Presence of Heart Disease and
# Maximum Recorded Heart Rate
sns.set(
    style="whitegrid",
    palette="colorblind",
    rc={
        "figure.figsize": (12, 8),
        "axes.titlesize": 18,
        "axes.labelsize": 16,
        "xtick.labelsize": 16,
        "ytick.labelsize": 16,
    },
)

f = sns.scatterplot(
    x="age", y="max_hr", hue="target", style="target", data=dataset
)
f.set_title("Presence of Heart Disease based on Age and Maximum Heart Rate")
plt.xlabel("Age")
plt.ylabel("Maximum Heart Rate")

Text(0, 0.5, 'Maximum Heart Rate')

dataset["age_category"] = pd.cut(dataset.age, bins=list(np.arange(25, 85, 5)))

plt.subplot(121)
dataset[dataset.target == 1].groupby("age_category")["age"].count().plot(
    kind="bar"
)
plt.title("Present")
plt.xlabel("Age Group")
plt.ylabel("Count")
plt.subplot(122)
dataset[dataset.target == 0].groupby("age_category")["age"].count().plot(
    kind="bar"
)
plt.title("Absent")
plt.xlabel("Age Group")
plt.ylabel("Count")

Text(0, 0.5, 'Count')

Plotting the Relationship between the Presence of Heart Disease and the Cholesterol Column

g = sns.scatterplot(
    x="age", y="chol", hue="target", style="target", data=dataset
)
g.set_title("Presence of Heart Disease based on Age and Cholesterol")
plt.xlabel("Age")
plt.ylabel("Cholesterol")

Text(0, 0.5, 'Cholesterol')

dataset["chol_cat"] = pd.cut(dataset.chol, bins=list(np.arange(120, 380, 20)))
dataset["chol_cat"] = pd.cut(dataset.chol, bins=list(np.arange(120, 380, 20)))

plt.subplot(121)
dataset[dataset.target == 1].groupby("chol_cat")["chol"].count().plot(
    kind="bar"
)
plt.title("Present")
plt.xlabel("Cholesterol Group")
plt.ylabel("Count")

plt.subplot(122)
dataset[dataset.target == 0].groupby("chol_cat")["chol"].count().plot(
    kind="bar"
)
plt.title("Absent")
plt.xlabel("Cholesterol Group")
plt.ylabel("Count")

Text(0, 0.5, 'Count')