<jupyter_start><jupyter_text>$\beta_0 = ?$<jupyter_code>#import numpy as np

#predicted_value = multiple_reg.predict()
#residuals = data['Price']-predicted_value
#print(predicted_value)


#np.set_printoptions(precision = 2)
#print(np.concatenate((predicted_value.reshape(-1,1),data['Price'].reshape(-1,1)),1))


#print('predicted_value')
#print(predicted_value)

#print('residuals')
#print(residuals)

#plt.figure(figsize=(8,8))
#plt.subplot(121)
#probplot(residuals, plot=plt)

#plt.subplot(122)
#sns.distplot(residuals, bins=100)
#plt.title("Histogram of the residuals")
#plt.xlabel("Residuals")
#plt.show()
# Plot regression against actual data
#plt.figure(figsize=(12, 6))
#plt.plot(adver['TV'], advert['Sales'], 'o')           # scatter plot showing actual data
#plt.plot(advert['TV'], pridicted_reg, 'r', linewidth=2)   # regression line
#plt.xlabel('TV Advertising Costs')
#plt.ylabel('Sales')
#plt.title('TV vs Sales')

#plt.show()


#plt.figure('Ethereum Price History')
#plt.title('Ethereum Price History on a daily basis')
#plt.xlabel('Date')
#plt.ylabel('Stock Price in U.S. Dollers')
#plt.grid(True)
#plt.plot(Eth_date, Eth_price, 'o')
#plt.plot(Eth_date, predicted_value, 'r', linewidth=2)   # regression line
##plt.gcf().autofmt_xdate()

#plt.show()

#x = data['Price']

#print(x)

#test = pd.DataFrame({'Actual Value' :data['Price'], 'Predicted Value' :predicted_value, 'Difference' :x-predicted_value})
#test[1989:1993]
#print(test)
#plt.figure('Ethereum Price History')
#plt.title('Ethereum Price Prediction using Linear Regrssion')
#plt.xlabel('Opening Price')
#plt.ylabel('Closing Price')
#plt.grid(True)
#plt.plot(Eth_date, Eth_price, 'o')
#plt.plot(data['Price'], data['Open'], 'r', linewidth=2)   # regression line
#plt.gcf().autofmt_xdate()
#new_date = 10
#new_date = pd.to_datetime(new_date)

#model.predict(new_date)
#from sklearn.model_selection import train_test_split
#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)
## Importing the Linear Regression Class
#from sklearn.linear_model import LinearRegression

## Creating an object of the Linear Regression Class
#regressor = LinearRegression()



## Fit the created object to our training set
#regressor.fit(x_train, y_train)
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