EDA and Data Cleaning

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

Data from https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction

# Import raw data
raw_data = pd.read_csv('../data/heart.csv')
raw_data.head()

	Age	Sex	ChestPainType	RestingBP	Cholesterol	FastingBS	RestingECG	MaxHR	ExerciseAngina	Oldpeak	ST_Slope	HeartDisease
0	40	M	ATA	140	289	0	Normal	172	N	0.0	Up	0
1	49	F	NAP	160	180	0	Normal	156	N	1.0	Flat	1
2	37	M	ATA	130	283	0	ST	98	N	0.0	Up	0
3	48	F	ASY	138	214	0	Normal	108	Y	1.5	Flat	1
4	54	M	NAP	150	195	0	Normal	122	N	0.0	Up	0

Check for NaN values in columns

nan_count = raw_data.isna().sum()
print(nan_count)

Age               0
Sex               0
ChestPainType     0
RestingBP         0
Cholesterol       0
FastingBS         0
RestingECG        0
MaxHR             0
ExerciseAngina    0
Oldpeak           0
ST_Slope          0
HeartDisease      0
dtype: int64

raw_data.hist(figsize=(14, 10))
plt.tight_layout()
plt.savefig('../figures/eda_histograms.png')

Looking at class distributions

fig,axes = plt.subplots(nrows=3,ncols=5,figsize=(16,10))

# loop through each column in the dataframe
for i, col in enumerate(raw_data.columns[:-1]):
    # select the appropriate subplot axis based on the index i
    ax = axes[i // 5, i % 5]
    # plot a histogram of the column for each group in 'HeartDisease'
    raw_data.groupby('HeartDisease')[col].hist(alpha=0.4, ax=ax)
    # set the title, y-axis label, and x-axis label for the subplot
    ax.set_title(col)
    ax.set_ylabel("Count")
    ax.set_xlabel("Value")
    ax.legend(raw_data['HeartDisease'])

fig.delaxes(axes[2][1])
fig.delaxes(axes[2][2])
fig.delaxes(axes[2][3])
fig.delaxes(axes[2][4])
# adjust the spacing between the subplots for better readability
plt.tight_layout()
plt.savefig('../figures/eda_class_distributions.png')

PCA

categorical_columns= ['Sex', 'ChestPainType','RestingECG', 'ExerciseAngina', 'ST_Slope' ]
X = raw_data.drop(columns=categorical_columns)
y = raw_data['HeartDisease']

scaler = StandardScaler()
X_standardized = scaler.fit_transform(X)

# Using PCA
pca = PCA(n_components = 7)
pca.fit(X_standardized)

PCA(n_components=7)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

p_c = pca.fit_transform(X_standardized)
# print(principalComponents)

principal_df = pd.DataFrame(data=p_c, columns = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7'])

final_df = pd.concat([principal_df, y],axis=1)
final_df.head()

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	HeartDisease
0	-2.417053	-0.508044	-0.536672	0.492483	-0.592140	0.263087	0.035454	0
1	0.138073	-0.813523	-0.245526	0.495047	-1.577625	-0.012082	0.836385	1
2	-1.346575	0.120716	0.050249	-1.194085	-0.405928	1.707199	-1.379748	0
3	0.768445	-0.416296	0.876559	-0.468257	-0.780828	0.872467	-0.231420	1
4	-0.519631	-0.324544	-1.160150	-1.158588	-0.430199	0.032315	-0.483574	0

# Plot
fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1) 
ax.set_xlabel('Principal Component 1', fontsize = 15)
ax.set_ylabel('Principal Component 2', fontsize = 15)
ax.set_title('2 Component PCA of Non Categorical Data', fontsize = 20)
targets = [0,1]
colors = ['r', 'b']
for target, color in zip(targets,colors):
    indicesToKeep = final_df['HeartDisease'] == target
    ax.scatter(final_df.loc[indicesToKeep, 'PC1']
               , final_df.loc[indicesToKeep, 'PC2']
               , c = color
               , s = 50)
ax.legend(targets)
ax.grid()
plt.savefig('../figures/2_component_pca.png')

## Variability of components
cum_var = pca.explained_variance_ratio_.cumsum()
cum_var=np.insert(cum_var,0,0)
k_features = list(range(0,X.shape[1]+1))

# Plot figure
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1,1,1)
ax.plot(k_features,cum_var)
# plt.rc('axes', titlesize=14)
# plt.rc('axes', labelsize=12)
ax.set_xlabel("Number of Eigenvalues")
ax.set_ylabel("Cumulative Sum of Variance")
ax.set_title("Cumulative Sum of Variance vs Number of Eigenvalues")
ax.set_xlim([0,7])
ax.set_ylim([0,1.01])
ax.grid()
plt.savefig('../figures/sum_of_variance.png')