Simple Machine Learning Program in Python to Use Linear Regression to predict House Price.

Requirement: The main requirement is to be able to assign and predict House Price in a given location based on age of house, distance from Airport and number of conveniences stores within 5 km radius.

Python IDE for Machine Learning used for the program: Google Colab

Dataset: RealEstate.xlsx

Let’s start coding:

import pandas
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from matplotlib import pylab
from pylab import *

# Read data and excel source and store it in Dataframe.
df = pandas.read_excel("/content/RealEstate.xlsx")

#Display the Dataframe
display(df)

# Remove rows and columns with Null/NaN values.
df=df.dropna(how='any')

# Since we want to predict the HousePrice based on HouseAge, DistanceFromAirport and NumberOfConvenienceStores. Remove all other columns from the Dataset.
df= df.drop(columns=['No','TransactionDate','Latitude','Longitude'])


# Select the column to predict.
y=df.pop('HousePriceinThousands')

# Select the columns based on which prediction will be done.
x =df.values

#Dsiplay the y value
display(y)

#Let's divide the dataset into two parts, one for training and other for testing.
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.33, test_size=0.67, random_state=100)

#Selecting the Algorithm for the model: Linear Regression
HousePriceModel = linear_model.LinearRegression(normalize=True)

#Fitting the Model
HousePriceModel.fit(x_train, y_train)

#Pridicting the House Price
HousePrice_predicted= HousePriceModel.predict(x_test)

#Display the Pridicted value
display(HousePrice_predicted)

#Choosing the Graph type
pylab.scatter(HousePrice_predicted,y_test)

#Applying labels
pylab.xlabel('Predicted')
pylab.ylabel('Actual')

Thank you and Happy Learning.

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