INTRODUCTION:

About my project ["PREDECTING REAL ESTATE PRICE USING LINEAR REGRESSION AND SUPPORT VECTOR Machine"] http://localhost:8892/notebooks/real%20estate%20project%20graphs.ipynb

Librarey's that are used in project :

DOMAIN --->

! ! !

DATA PREPROCESSING --->

1.Need to import libraries and modules in to the program

import numpy as nm

import matplotlib.pyplot as mtp

import pandas as pd

2.we have to load the dataset

data_set = pd.read_cv("BENGULAR_DATASET")

3. Extract the dependent and independent variables into data set for location(iloc: stands for index location)

x = data_set.iloc[::-1]

y = data_set.iloc[::1]

4. split the dataset into two parts for testing and training

#sklearn.library

from sklearn.model_selection import train_test_spilt:

x_train,x_test,y_test,y_train = train_test_split(x,y,test_size=0.3, random_state=5)

5. FIT THE LINEAR REGRESSIONFOR TRAING DATA

from sklearn.model import Linearregression;

regressor = LinearRegression;

regressor.fit(x_train,y_train)

6. predict the value's that correct output or not

y_predict = regressor.predict(x_test)

x_predict = regressor.predict(y_train)

7. we will go for validation for traing dataset;

mtp.scatter()(x_train,y_train,colour = "Green");

mtp.plot(x_train,y_predict,colour = "Red");

mtp.title("bath, totalsqfoot, bhk vs "prices")

mtp.x_label("Toatlsqfoot, bhk, bath");

mtp.y_label("prices");

8. then after we have to do the validation on testing the same instead of x_train,y_train we use x_test, y_test;

conclusion: linear regression is the Best fit model execution

support vector machine process in machine learning

from sklearn.model import svc(classifer)

classifer = support vector machine

classifier.fit(x_train,y_train)

2. predict the values

y_train = classier.fit(x_train)

3. validation on train set results

4. validation on test set results

5. CONCLUSION :

**Finally after doing two Algorithm executions we got the best fit model performance is "SUPPORT VECTOR MACHINE"(SVM) . We got the predected values and actual values are equal in svm model

    [    support vector machine accuracy more than the linear regression ]

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