QuestionQuestion

Develop a python program that predicts the housing prices in various counties using linear regression.

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#   Importing required packages
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score


#   This class contains various methods performing
#       - Reading data from file.
#       - Printing summary of the data
#       - Plotting data
#       - Fitting linear regression to predict house price of a county
class HousePriceLinearRegression:
   
    #   Default constructor of the class,
    #   it calls read_data method to get data from excel.
    def __init__(self):
       self.read_data()

    #   This method reads excel file, and stores the data in the instance variable.
    def read_data(self):
       self.data = pd.read_excel("2019-MedianPricesofExistingDetachedHomesHistoricalData.xlsx",
                                  skiprows=7)

    #   This method displays the summary of the data
    def print_summary(self):
       try:
            print(self.data.head())
       except:
            print("Data is not available")

    #   This method takes x, y, and predicted data as the required features,
    #   and plots the scatter plot and fitted line on the plot.
    def plot_data(self, x_data, y_data, pred, xlab="CA", ylab="Alameda"):
       plt.scatter(x_data, y_data...

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