import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import warnings
import graphviz
warnings.filterwarnings('ignore')
%matplotlib inline
import os
from sklearn.cluster import KMeans
#Importing the dataset
Diabetes2 = pd.read_csv('C:\Users\PPP\Desktop\Python_Practice\Datasets\Diabetes\diabetic_data.csv', index_col=False)
#Split into input and output features
y = Diabetes2["readmittedFL"]
X = Diabetes2[["time_in_hospital","num_lab_procedures","num_procedures","num_medications",
"number_outpatient","number_emergency","number_inpatient","number_diagnoses"]]
X.head()
y.head()
#Select the annual income and the spending score columns
KMeans = KMeans()
X_array=X.iloc[0:8:1].values
y_kmeans = KMeans.predict(X_array)
plt.scatter(X_array[:, 0], X_array[:, 1], c=y_kmeans, s=50, cmap='viridis')
I just started practicing KMeans where I am getting this error. I can not understand what is wrong.
When I run the command y_kmeans = KMeans.predict(X_array)
,
it was showing an error
TypeError: predict() missing 1 required positional argument: X
How can I solve this error?
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