Real Estate Price Prediction Using Machine Learning With GUI Using Tkinter

 Real Estate Price Prediction Using Machine Learning With GUI   Using Tkinter

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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()