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Severity_Level_Prediction/Severity_Level_Prediction.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# Severity Level Prediction
+
+# In[1]:
+
+
+#Importing Libraries
+from mpl_toolkits.mplot3d import Axes3D
+from sklearn.preprocessing import StandardScaler
+import matplotlib.pyplot as plt
+from tkinter import *
+import numpy as np
+import pandas as pd
+import os
+
+
+# In[2]:
+
+
+#List of the symptoms is listed here in list l1.
+l1=['Yeast_Visibility-ears,armpits,_or_paws','Yeast_Visibility-around_half_of_the_body','Yeast_Visibility-whole_body',
+    'Redness-pink_or_red_skin','Redness-red_skin','Redness-gray_or_black_skin',
+   'No_unpleasant_Smell','Musty_smell','Extreme_musty_smell',
+   'No_Itching_and_Scratching','Itching_and_Scratching-time_to_time','Itching_and_Scratching-constantly',
+   'No_Thickened_Skin','Thickened_Skin','Extremely_Thickened_Skin(Elephant_skin_appearance)',
+   'No_Hair_Loss','Starting_Hair_Loss','Hair_Loss',
+   'No_Hearing_Issues','Hearing_Issues','Deafness']
+
+
+# In[3]:
+
+
+#List of Severity Levels.
+level=['Primary','Secondary','Tertiary']
+
+#level = [df['Prediction'].unique()]
+
+
+# In[4]:
+
+
+l2=[]
+for i in range(0,len(l1)):
+    l2.append(0)
+print(l2)
+
+
+# In[5]:
+
+
+#Reading the training.csv file
+df=pd.read_csv("severity level.csv")
+DF=pd.read_csv("severity level.csv" , index_col='Prediction')
+
+#Replace the values in the imported file by pandas by the inbuilt function replace in pandas.
+
+df.replace({'Prediction':{'Primary':0,'Secondary':1,'Tertiary':2}},inplace=True)
+#df.head()
+DF.head()
+
+
+# In[6]:
+
+
+X= df[l1]
+y = df[["Prediction"]]
+np.ravel(y)
+print(X)
+
+
+# In[7]:
+
+
+#Reading the  testing.csv file
+tr=pd.read_csv("testing.csv")
+
+#Using inbuilt function replace in pandas for replacing the values
+
+tr.replace({'Prediction':{'Primary':0,'Secondary':1,'Tertiary':2}},inplace=True)
+tr.head()
+
+
+# In[8]:
+
+
+X_test= tr[l1]
+y_test = tr[["Prediction"]]
+np.ravel(y_test)
+print(X_test)
+
+
+# In[9]:
+
+
+print(y_test)
+
+
+# Decision Tree Algorithm
+
+# In[10]:
+
+
+root = Tk()
+pred1=StringVar()
+def DecisionTree():
+    if len(NameEn.get()) == 0:
+        pred1.set(" ")
+        comp=messagebox.askokcancel("System","Please fill the breed of your pet !")
+        if comp:
+            root.mainloop()
+    elif((Symptom1.get()=="Select Here") or (Symptom2.get()=="Select Here") ):
+        pred1.set(" ")
+        sym=messagebox.askokcancel("System","Please fill atleast first two Symptoms")
+        if sym:
+            root.mainloop()
+    else:
+        from sklearn import tree
+
+        clf3 = tree.DecisionTreeClassifier() 
+        clf3 = clf3.fit(X,y)
+
+        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
+        y_pred=clf3.predict(X_test)
+        print("Decision Tree")
+        print("Accuracy")
+        print(accuracy_score(y_test, y_pred))
+        print(accuracy_score(y_test, y_pred,normalize=False))
+        print("Confusion matrix")
+        conf_matrix=confusion_matrix(y_test,y_pred)
+        print(conf_matrix)
+
+        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),Symptom6.get(),Symptom7.get()]
+
+        for k in range(0,len(l1)):
+            for z in psymptoms:
+                if(z==l1[k]):
+                    l2[k]=1
+        inputtest = [l2]
+        predict = clf3.predict(inputtest)
+        predicted=predict[0]
+
+        h='no'
+        for a in range(0,len(level)):
+            if(predicted == a):
+                h='yes'
+                break
+
+    
+        if (h=='yes'):
+            pred1.set(" ")
+            pred1.set(level[a])
+        else:
+            pred1.set(" ")
+            pred1.set("Not Found")
+
+
+# Random Forest Algorithm
+
+# In[11]:
+
+
+pred2=StringVar()
+def randomforest():
+    if len(NameEn.get()) == 0:
+        pred1.set(" ")
+        comp=messagebox.askokcancel("System","Please fill the breed of your pet !")
+        if comp:
+            root.mainloop()
+    elif((Symptom1.get()=="Select Here") or (Symptom2.get()=="Select Here")):
+        pred1.set(" ")
+        sym=messagebox.askokcancel("System","Please fill atleast first two Symptoms")
+        if sym:
+            root.mainloop()
+    else:
+        from sklearn.ensemble import RandomForestClassifier
+        clf4 = RandomForestClassifier(n_estimators=100)
+        clf4 = clf4.fit(X,np.ravel(y))
+
+        # calculating accuracy 
+        from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
+        y_pred=clf4.predict(X_test)
+        print("Random Forest")
+        print("Accuracy")
+        print(accuracy_score(y_test, y_pred))
+        print(accuracy_score(y_test, y_pred,normalize=False))
+        print("Confusion matrix")
+        conf_matrix=confusion_matrix(y_test,y_pred)
+        print(conf_matrix)
+    
+        psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get(),Symptom6.get(),Symptom7.get()]
+
+        for k in range(0,len(l1)):
+            for z in psymptoms:
+                if(z==l1[k]):
+                    l2[k]=1
+
+        inputtest = [l2]
+        predict = clf4.predict(inputtest)
+        predicted=predict[0]
+        
+        h='no'
+        for a in range(0,len(level)):
+            if(predicted == a):
+                h='yes'
+                break
+        if (h=='yes'):
+            pred2.set(" ")
+            pred2.set(level[a])
+        else:
+            pred2.set(" ")
+            pred2.set("Not Found")
+
+
+# Building Graphical User Interface
+
+# In[12]:
+
+
+#Tk class is used to create a root window
+root.configure(background='Ivory')
+root.title('Skin Disease Severity Level Predictor System')
+root.resizable(0,0)
+
+
+# In[13]:
+
+
+Symptom1 = StringVar()
+Symptom1.set("Select Here")
+
+Symptom2 = StringVar()
+Symptom2.set("Select Here")
+
+Symptom3 = StringVar()
+Symptom3.set("Select Here")
+
+Symptom4 = StringVar()
+Symptom4.set("Select Here")
+
+Symptom5 = StringVar()
+Symptom5.set("Select Here")
+
+Symptom6 = StringVar()
+Symptom6.set("Select Here")
+
+Symptom7 = StringVar()
+Symptom7.set("Select Here")
+Name = StringVar()
+
+
+# In[14]:
+
+
+prev_win=None
+def Reset():
+    global prev_win
+
+    Symptom1.set("Select Here")
+    Symptom2.set("Select Here")
+    Symptom3.set("Select Here")
+    Symptom4.set("Select Here")
+    Symptom5.set("Select Here")
+    Symptom6.set("Select Here")
+    Symptom7.set("Select Here")
+    NameEn.delete(first=0,last=100)
+    pred1.set(" ")
+    pred2.set(" ")
+    try:
+        prev_win.destroy()
+        prev_win=None
+    except AttributeError:
+        pass
+
+
+# In[15]:
+
+
+from tkinter import messagebox
+def Exit():
+    qExit=messagebox.askyesno("System","Do you want to exit the system ?")
+    
+    if qExit:
+        root.destroy()
+        exit()
+
+
+# In[16]:
+
+
+#Headings for the GUI written at the top of GUI
+w2 = Label(root, justify=LEFT, text="Skin Disease Severity Level Prediction", fg="Black", bg="Ivory")
+w2.config(font=("Times",30,"bold"))
+w2.grid(row=1, column=0, columnspan=2, padx=100)
+
+
+# In[17]:
+
+
+#Label for the name
+NameLb = Label(root, text="Breed of the Pet *", fg="Dark Blue", bg="Ivory")
+NameLb.config(font=("Times",15,"bold"))
+NameLb.grid(row=6, column=0, pady=15, sticky=W)
+
+
+# In[18]:
+
+
+#Creating Labels for the symtoms
+S1Lb = Label(root, text="Symptom 1 *", fg="Black", bg="Ivory")
+S1Lb.config(font=("Times",15,"bold"))
+S1Lb.grid(row=7, column=0, pady=10, sticky=W)
+
+S2Lb = Label(root, text="Symptom 2 *", fg="Black", bg="Ivory")
+S2Lb.config(font=("Times",15,"bold"))
+S2Lb.grid(row=8, column=0, pady=10, sticky=W)
+
+S3Lb = Label(root, text="Symptom 3", fg="Black",bg="Ivory")
+S3Lb.config(font=("Times",15,"bold"))
+S3Lb.grid(row=9, column=0, pady=10, sticky=W)
+
+S4Lb = Label(root, text="Symptom 4", fg="Black", bg="Ivory")
+S4Lb.config(font=("Times",15,"bold"))
+S4Lb.grid(row=10, column=0, pady=10, sticky=W)
+
+S5Lb = Label(root, text="Symptom 5", fg="Black", bg="Ivory")
+S5Lb.config(font=("Times",15,"bold"))
+S5Lb.grid(row=11, column=0, pady=10, sticky=W)
+
+S6Lb = Label(root, text="Symptom 6", fg="Black", bg="Ivory")
+S6Lb.config(font=("Times",15,"bold"))
+S6Lb.grid(row=12, column=0, pady=10, sticky=W)
+
+S7Lb = Label(root, text="Symptom 7", fg="Black", bg="Ivory")
+S7Lb.config(font=("Times",15,"bold"))
+S7Lb.grid(row=13, column=0, pady=10, sticky=W)
+
+
+# In[19]:
+
+
+#Labels for the different algorithms
+lrLb = Label(root, text="DecisionTree", fg="black", bg="Light blue", width = 20)
+lrLb.config(font=("Times",15,"bold"))
+lrLb.grid(row=15, column=0, pady=10,sticky=W)
+
+destreeLb = Label(root, text="RandomForest", fg="black", bg="Light blue", width = 20)
+destreeLb.config(font=("Times",15,"bold"))
+destreeLb.grid(row=17, column=0, pady=10, sticky=W)
+OPTIONS = sorted(l1)
+
+
+# In[20]:
+
+
+#Taking breed-name as input from user
+NameEn = Entry(root, textvariable=Name)
+NameEn.grid(row=6, column=1)
+
+#Taking Symptoms as input from the dropdown from the user
+S1 = OptionMenu(root, Symptom1,*OPTIONS)
+S1.grid(row=7, column=1)
+
+S2 = OptionMenu(root, Symptom2,*OPTIONS)
+S2.grid(row=8, column=1)
+
+S3 = OptionMenu(root, Symptom3,*OPTIONS)
+S3.grid(row=9, column=1)
+
+S4 = OptionMenu(root, Symptom4,*OPTIONS)
+S4.grid(row=10, column=1)
+
+S5 = OptionMenu(root, Symptom5,*OPTIONS)
+S5.grid(row=11, column=1)
+
+S6 = OptionMenu(root, Symptom6,*OPTIONS)
+S6.grid(row=12, column=1)
+
+S7 = OptionMenu(root, Symptom7,*OPTIONS)
+S7.grid(row=13, column=1)
+
+
+# In[21]:
+
+
+#Buttons for predicting the severity level using different algorithms
+dst = Button(root, text="Prediction 1", command=DecisionTree,bg="Blue",fg="yellow")
+dst.config(font=("Times",15,"bold"))
+dst.grid(row=6, column=3,padx=10)
+
+rnf = Button(root, text="Prediction 2", command=randomforest,bg="Blue",fg="yellow")
+rnf.config(font=("Times",15,"bold"))
+rnf.grid(row=7, column=3,padx=10)
+
+rs = Button(root,text="Reset Inputs", command=Reset,bg="yellow",fg="purple",width=12)
+rs.config(font=("Times",15,"bold"))
+rs.grid(row=10,column=3,padx=10)
+
+ex = Button(root,text="Exit System", command=Exit,bg="yellow",fg="purple",width=12)
+ex.config(font=("Times",15,"bold"))
+ex.grid(row=11,column=3,padx=10)
+
+
+# In[22]:
+
+
+#Showing the output of different algorithms
+t1=Label(root,font=("Times",15,"bold"),text="Decision Tree",height=1,bg="Light green"
+         ,width=40,fg="black",textvariable=pred1,relief="sunken").grid(row=15, column=1, padx=10)
+
+t2=Label(root,font=("Times",15,"bold"),text="Random Forest",height=1,bg="Light green"
+         ,width=40,fg="black",textvariable=pred2,relief="sunken").grid(row=17, column=1, padx=10)
+
+
+# In[23]:
+
+
+#calling this function because the application is ready to run
+root.mainloop()
+
+
+# In[ ]:
+
+
+
+