MLAIPractical27.py

import pandas as pd
from sklearn import linear_model

#Function to get data
def get_data(file_name):
    data = pd.read_csv(file_name)
    flash_x_parameter = []
    flash_y_parameter = []
    got_x_parameter = []
    got_y_parameter =[]
    for x1,y1,x2,y2 in zip(data['Flash_episode_number'],
                           data['Flash_us_viewers'],
                           data['GoT_episode_number'],
                           data['GoT_us_viewers']):
        flash_x_parameter.append([float(x1)])
        flash_y_parameter.append(float(y1))
        got_x_parameter.append([float(x2)])
        got_y_parameter.append(float(y2))
    return flash_x_parameter, flash_y_parameter,\
        got_x_parameter, got_y_parameter

def find_more_viewers(x1,y1,x2,y2):
    regr1= linear_model.LinearRegression()
    regr1.fit(x1,y1) #Train machine
    predicted_value1= regr1.predict([[10]])
    print("Prediction of Flash : ", predicted_value1)
    regr2 = linear_model.LinearRegression()
    regr2.fit(x2,y2)
    predicted_value2= regr2.predict([[10]])
    print("Prediction of Got : ", predicted_value2)
    if(predicted_value1 > predicted_value2):
        print("Next episode of Flash Tv Show will have"
                         "more viewers for next week")
    else:
        print("Next episode of Got Tv Show"
              "will have more viewers for next week")

if __name__ == "__main__":
    x1,y1,x2,y2 = get_data('LR_Episodes.csv')
    find_more_viewers(x1,y1,x2,y2)