def kmeansModel(self):
print("build the model and Get Visualization")
print("Number of clusters k : ", self.e1.get())
print("Number of runs : ", self.e2.get())
if self.e1.get() == "" or self.e2.get() == "":
messagebox.showinfo("Error", "One of the parameters is missing ")
else:
if self.bool:
if self.e1.get().isdigit() and self.e2.get().isdigit():
if int(self.e1.get()) > 2 and int(
self.e1.get()) < 165 and int(self.e2.get()) < 50:
clustering = KMeansClustering.Clustering(
self.preprocess.data_frame)
clustering.activate_k_means_algorithm(
int(self.e1.get()), int(self.e2.get()))
clustering.create_scatter_generosity_social_support()
clustering.create_country_map()
messagebox.showinfo(
"clustering", "Clustering completed successfully!")
self.showImg()
else:
messagebox.showinfo(
"Error",
"One of the parameters doesn't make sense \n"
"The k must be between 2 and 165 (number of countries)\n"
"And Number of runs must be under 50 ")
else:
messagebox.showinfo("Error",
"The parameters must be numbers !")
else:
messagebox.showinfo("Error", "Must first do pre-processing ")
