# 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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