ax3.grid(True)
ax3.legend(handles=[blue_patch, red_patch])
plt.show()
# Unbalanced Feature's Correlation Matrix
fig, (ax1,ax2) = plt.subplots(2, 1, figsize=(24,20))
sns.heatmap(test.corr(), cmap= 'coolwarm_r', annot_kws={'size':20}, ax=ax1)
ax1.set_title("Imbalanced Correlation Matrix \n (don't use for reference) ", fontsize=14)
# Balanced Feature's Correlation Matrix
over_sampled = pd.DataFrame(X_resampled)
over_sampled.insert(98, 'Class',Y_resampled,True)
sns.heatmap(X_resampled.corr(), cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)
ax2.set_title("OverSample Correlation Matrix \n (use for reference)", fontsize=14)
plt.show()
# Feature distrubutions
f, ( (ax1, ax2, ax3, ax4) , (ax5, ax6, ax7, ax8), (ax9, ax10, ax11, ax12), (ax13, ax14, ax15, ax16)) = plt.subplots(4, 4, figsize=(24,24))
'''feature = ['Variance','Mobility','Complexity','Max_FFT_AR','Skewness','Kurtosis' ]'''
feat =feature[1]
'''Inter=0 - Pre=1 Class'''
IPC=0
cl = features.index[features.Class == IPC].tolist()
