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Worked Algo For Linear Regression (Python 2.7.x and 3.4.x)

RussianDeveloper Oct 7th, 2015 (edited) 282 Never
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  1. # coding=utf-8
  2. # Linear Regression Realization (http://dataaspirant.com/2014/12/20/linear-regression-implementation-in-python/)
  3.  
  4. # input_data.csv:
  5. # square_feet;price
  6. # 150;6450
  7. # 200;7450
  8. # 250;8450
  9. # 300;9450
  10. # 350;11450
  11. # 400;15450
  12. # 600;18450
  13.  
  14. # Required Packages
  15. import matplotlib.pyplot as plt
  16. import pandas as pd
  17. from sklearn import linear_model
  18.  
  19.  
  20. # Function to get data
  21. def get_data(file_name):
  22.     data = pd.read_csv(file_name, sep=";")
  23.     x_parameter = []
  24.     y_parameter = []
  25.     # TODO: Replace the names of the fields 'square foot', 'price' for your own values
  26.     for single_square_feet in data['square_feet']:
  27.         x_parameter.append([float(single_square_feet)])
  28.  
  29.     for single_price_value in data['price']:
  30.         y_parameter.append(float(single_price_value))
  31.     return x_parameter, y_parameter
  32.  
  33.  
  34. # Function for Fitting our data to Linear model
  35. # noinspection PyPep8Naming
  36. def linear_model_main(x_parameters, y_parameters, predict_value):
  37.     # Create linear regression object
  38.     regr = linear_model.LinearRegression()
  39.     regr.fit(x_parameters, y_parameters)
  40.     # noinspection PyArgumentList
  41.     predict_outcome = regr.predict(predict_value)
  42.     predictions = {'intercept': regr.intercept_, 'coefficient': regr.coef_, 'predicted_value': predict_outcome}
  43.     return predictions
  44.  
  45.  
  46. # Function to show the resutls of linear fit model
  47. def show_linear_line(x_parameters, y_parameters):
  48.     # Create linear regression object
  49.     regr = linear_model.LinearRegression()
  50.     regr.fit(x_parameters, y_parameters)
  51.     plt.scatter(x_parameters, y_parameters, color='blue')
  52.     # noinspection PyArgumentList
  53.     plt.plot(x_parameters, regr.predict(x_parameters), color='red', linewidth=4)
  54.     # Supress axis value
  55.     plt.xticks(())
  56.     plt.yticks(())
  57.     plt.show()
  58.  
  59.  
  60. X, Y = get_data('input_data.csv')
  61. predicted_value = 700
  62. result = linear_model_main(X, Y, predicted_value)
  63. print('Constant Value: {0}'.format(result['intercept']))
  64. print('Coefficient: {0}'.format(result['coefficient']))
  65. print('Predicted Value: {0}'.format(result['predicted_value']))
  66. show_linear_line(X, Y)
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