python-[panda]-[sklearn]-[matplotlib]-Linear Prediction

Article:

http://python.jobbole.com/81215/

Python's library of functions is so powerful! After reading this blog will never use MATLAB ~ ~

This article uses "panda" to read the CSV data, use the Linear_model in "Sklearn" to train the model and make a linear prediction using the "matplotlib" The fitting situation is represented by a graph.

The table below is the table used to train the model:

The code is as follows:

#-*-coding:utf-8-*-" "Created on 2016/11/26@author:chensi" "#Required PackagesImportMatplotlib.pyplot as PltImportNumPy as NPImportPandas as PD fromSklearnImportdatasets, Linear_model fromNumpy.ma.coreImportGetData#Function to get DatadefGet_data (file_name): Data=Pd.read_excel (file_name) X_parameter=[] Y_parameter= []     forSingle_square_feet, Single_price_valueinchZip (data['Square_feet'],data[' Price']): X_parameter.append ([Float (single_square_feet)]) y_parameter.append (float (single_price_value)) returnX_parameter,y_parameter#Function for Fitting we data to Linear modeldefLinear_model_main (x_parameters,y_parameters,predict_value):#Create Linear Regression ObjectRegr =Linear_model. Linearregression () Regr.fit (X_parameters, y_parameters) predict_outcome=regr.predict (predict_value) predictions={} predictions['Intercept'] =Regr.intercept_ predictions['coefficient'] =regr.coef_ predictions['Predicted_value'] =Predict_outcomereturnpredictions#Function to show the resutls of linear fit modeldefShow_linear_line (x_parameters,y_parameters):#Create Linear Regression ObjectRegr =Linear_model. Linearregression () Regr.fit (X_parameters, Y_parameters) plt.scatter (X_parameters,y_parameters,color='Blue') Plt.plot (X_parameters,regr.predict (x_parameters), Color='Red', linewidth=4) Plt.xticks (()) Plt.yticks (()) plt.show ()#---------Test---------------#----------------------------X, y = Get_data ("G:/input_data.csv") show_linear_line (x, y)Print(Linear_model_main (x,y,150))#----------------------------#----------------------------

Graph of output:

Example two:

Code:

#-*-coding:utf-8-*-" "Created on 2016/11/26 @author: Chensi" "#Required PackagesImportCSVImportSYSImportMatplotlib.pyplot as PltImportNumPy as NPImportPandas as PD fromSklearnImportdatasets, Linear_model#Function to get DatadefGet_data (file_name): Data=Pd.read_excel (file_name) Flash_x_parameter=[] Flash_y_parameter=[] Arrow_x_parameter=[] Arrow_y_parameter= []     forX1,y1,x2,y2inchZip (data['Flash_episode_number'],data['flash_us_viewers'],data['Arrow_episode_number'],data['arrow_us_viewers']): Flash_x_parameter.append ([Float (x1)]) flash_y_parameter.append (float (y1)) Arrow_x_parameter.a Ppend ([Float (x2)]) arrow_y_parameter.append (float (y2) )returnFlash_x_parameter,flash_y_parameter,arrow_x_parameter,arrow_y_parameter#Function to know which Tv show 'll has more viewersdefmore_viewers (x1,y1,x2,y2): Regr1=Linear_model. Linearregression () regr1.fit (x1, y1) predicted_value1= Regr1.predict (9)    Print(predicted_value1) regr2=Linear_model. Linearregression () Regr2.fit (x2, y2) predicted_value2= Regr2.predict (9)#Print predicted_value1#Print Predicted_value2    ifPredicted_value1 >predicted_value2:Print("The Flash Tv Show would has more than viewers for next week")    Else:        Print("Arrow Tv Show 'll has more viewers for next week") X1,y1,x2,y2= Get_data ('g:/input_data_2.xlsx')#Print X1,y1,x2,y2More_viewers (X1,y1,x2,y2)

Output:

python-[panda]-[sklearn]-[matplotlib]-Linear Prediction