DRUG PREDICTTION USING MACHINE LEARNING WITH GUI USING TKINTER

 DRUG PREDICTTION USING MACHINE LEARNING WITH GUI USING TKINTER

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Dataset - Drug 200.csv

Background Image - img

1. First Install Anaconda from Anaconda | The World's Most Popular Data Science Platform Website.
2. Now Open Sypder From Anaconda Navigator

CREATING BACKEND FILE [MAIN.PY]

import pandas as pd

df=pd.read_csv('drug200.csv')

#checking if there is any missing value

#print(df.isnull().sum())

x=df.iloc[0:201,0:5]#independent

y=df["Drug"]#dependent

from sklearn.preprocessing import LabelEncoder

la=LabelEncoder()

y=la.fit_transform(y)

ls=LabelEncoder()

x["Sex"]=ls.fit_transform(x["Sex"])

lb=LabelEncoder()

x["BP"]=lb.fit_transform(x["BP"])

lc=LabelEncoder()

x["Cholesterol"]=lc.fit_transform(x["Cholesterol"])

#scaling our independent data

from sklearn.preprocessing import StandardScaler

sc=StandardScaler()

x=sc.fit_transform(x)

#train test

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)

#RandomForestClassifier

from sklearn.ensemble import RandomForestClassifier

lr=RandomForestClassifier()

lr.fit(x_train,y_train)

y_pred=lr.predict(x_test)

#checking accuracy

from sklearn.metrics import accuracy_score

print("Accuracy Score is:")

print(accuracy_score(y_test, y_pred))

#saving models

from joblib import dump

dump(la,"Drug.joblib")

dump(ls,"Sex.joblib")

dump(lb,"BP.joblib")

dump(lc,"Cholesterol.joblib")

dump(sc,"scaling.joblib")

dump(lr,"regressor.joblib")

GUI USING TKINTER

import pandas as pd

from joblib import load

from tkinter import *

la=load("Drug.joblib")

ls=load("Sex.joblib")

lb=load("BP.joblib")

lc=load("Cholesterol.joblib")

sc=load("scaling.joblib")

lr=load("regressor.joblib")

def result():

    new=pd.DataFrame({"Age":[(int(a1.get()))],"Sex":[(a2.get())],"BP":[(a3.get())],"Cholesterol":[(a4.get())],"Na_to_K":[(float(a5.get()))]})

    new["Sex"]=ls.transform(new["Sex"])

    new["BP"]=lb.transform(new["BP"])

    new["Cholesterol"]=lc.transform(new["Cholesterol"])

    new=sc.transform(new)

    o=lr.predict(new)

    if o==0:

        drug=Label(root,text="Required Drug is [DrugY]",font=("arial",20),fg="white",bg="violet")

        drug.place(x=10,y=300)

    elif o==1:

        drug=Label(root,text="Required Drug is [drugA]",font=("arial",20),fg="white",bg="red")

        drug.place(x=10,y=300)

    elif o==2:

        drug=Label(root,text="Required Drug is [drugB]",font=("arial",20),fg="white",bg="green")

        drug.place(x=10,y=300)

    elif o==3:

        drug=Label(root,text="Required Drug is [drugC]",font=("arial",20),fg="white",bg="orange")

        drug.place(x=10,y=300)

    else:

        drug=Label(root,text="Required Drug is [drugX]",font=("arial",20),fg="white",bg="black")

        drug.place(x=10,y=300)

root=Tk()

root.geometry("1000x400")

root.resizable(0,0)

root.title("Drug Prediction")

a1=StringVar()

a2=StringVar()

a3=StringVar()

a4=StringVar()

a5=StringVar()

heading=Label(root,text="Drug Prediction Using ML",font=("Arial",25),fg="darkblue")

heading.place(x=0,y=10)

age=Label(root,text="Enter the Age")

age.place(x=10,y=80)

age1=Entry(root,textvariable=a1)

age1.place(x=250,y=80)

sex=Label(root,text="Enter the Gender(M/F)")

sex.place(x=10,y=110)

sex1=Entry(root,textvariable=a2)

sex1.place(x=250,y=110)

bp=Label(root,text="Enter the BP (HIGH/NORMAL/LOW)")

bp.place(x=10,y=140)

bp1=Entry(root,textvariable=a3)

bp1.place(x=250,y=140)

Cholesterol=Label(root,text="Enter the Cholesterol level(HIGH/NORMAL) ")

Cholesterol.place(x=10,y=170)

Cholesterol1=Entry(root,textvariable=a4)

Cholesterol1.place(x=250,y=170)

Na_to_K=Label(root,text="Enter the Na to K value")

Na_to_K.place(x=10,y=200)

Na_to_K1=Entry(root,textvariable=a5)

Na_to_K1.place(x=250,y=200)

submit=Button(root,text="Predict",command=result,font=("Arial",20),fg="white",bg="red")

submit.place(x=250,y=230)

bg = PhotoImage(file = r"img.png")

# Show image using label

label2 = Label( root, image = bg)

label2.place(x = 500, y = 0)

root.mainloop()

Real Estate Price Prediction Using Machine Learning With GUI Using Tkinter

 Real Estate Price Prediction Using Machine Learning With GUI   Using Tkinter

• Create a New Directory for your Project Using File Explorer

1. First Install Anaconda from Anaconda | The World's Most Popular Data Science Platform Website.
2. Now Open Sypder From Anaconda Navigator

import pandas as pd

df=pd.read_csv('data.csv')

df = df.drop(columns=["No"])

#checking if there is any missing value

#print(df.isnull().sum())

x= df.drop(columns=['Y house price of unit area'])

y=df['Y house price of unit area']

#changing string to numeric form

from sklearn.preprocessing import LabelEncoder

la=LabelEncoder()

y=la.fit_transform(y)

#scaling our independent data

from sklearn.preprocessing import StandardScaler

sc=StandardScaler()

x=sc.fit_transform(x)

#train test split

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)

#          -------------------- END OF PRE-PROCESSING---------------------           #

from sklearn.metrics import r2_score

#Random Forest Regressor Model

from sklearn.ensemble import RandomForestRegressor

rfc=RandomForestRegressor()

rfc.fit(x_train,y_train)

y_pred=rfc.predict(x_test)

#checking accuracy

print("Random Forest Classifier")

print(r2_score(y_test, y_pred))

#DUMPING MODELS

from joblib import dump

dump(la,"price.joblib")

dump(sc,"scaling.joblib")

dump(rfc,"randomforest.joblib")

MAKING GUI USING TKINTER

import pandas as pd

from joblib import load

from tkinter import *

from tkinter import messagebox

sc=load("scaling.joblib")

rfc=load("randomforest.joblib")

def result():

    try:

        new=pd.DataFrame({"X1 transaction date":[float(a1.get())],"X2 house age":[float(a2.get())],"X3 distance to the nearest MRT station":[float(a3.get())],"X4 number of convenience stores":[float(a4.get())],"X5 latitude":[float(a5.get())],"X6 longitude":[float(a6.get())]})

        new=sc.transform(new)

        res=rfc.predict(new)

        anslabel=Label(root,text='Price is',font=("Arial",10)).place(x=150,y=250)

        ans=Label(root,text=res,font=('Arial',10)).place(x=200,y=250)

        ans2=Label(root,text= "per unit area",font=("arial",10)).place(x=250,y=250)

    except:

        messagebox.showinfo("Error","Please fill all the values")

root=Tk()

root.geometry("400x300")

root.resizable(0,0)

root.title("Real Estate Prediction")

a1=StringVar()

a2=StringVar()

a3=StringVar()

a4=StringVar()

a5=StringVar()

a6=StringVar()

heading=Label(root,text="Real Estate Prediction System",font=("Arial",20),fg="PURPLE")

heading.place(x=10,y=5)

one=Label(root,text="Transaction date")

one.place(x=10,y=50)

two=Entry(root,textvariable=a1)

two.place(x=200,y=50)

three=Label(root,text="House age")

three.place(x=10,y=80)

four=Entry(root,textvariable=a2)

four.place(x=200,y=80)

five=Label(root,text="Distance to the nearest MRT station")

five.place(x=10,y=110)

six=Entry(root,textvariable=a3)

six.place(x=200,y=110)

seven=Label(root,text="Number of convenience stores")

seven.place(x=10,y=140)

eight=Entry(root,textvariable=a4)

eight.place(x=200,y=140)

nine=Label(root,text="Enter the Latitude")

nine.place(x=10,y=170)

ten=Entry(root,textvariable=a5)

ten.place(x=200,y=170)

nine=Label(root,text="Enter the Longitude")

nine.place(x=10,y=200)

ten=Entry(root,textvariable=a6)

ten.place(x=200,y=200)

submit=Button(root,text="PREDICT",bg="lightgrey",command=result,font=("Arial"),fg='black')

submit.place(x=10,y=250)

root.mainloop()