python-[panda]-[sklearn]-[matplotlib]-線性預測

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文章：

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

python的函數庫好強大！看完這篇博再也不會用matlab了~~

這篇文章使用【panda】讀取csv的資料，使用【sklearn】中的linear_model訓練模型並進行線性預測，使用【matplotlib】將擬合的情況用圖表示出來。

                   下面的表格是用於訓練模型的表格：

代碼如下：

# -*- coding: utf-8 -*-‘‘‘Created on 2016/11/26@author: chensi‘‘‘# Required Packagesimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom sklearn import datasets, linear_modelfrom numpy.ma.core import getdata# Function to get datadef get_data(file_name):    data = pd.read_excel(file_name)    X_parameter = []    Y_parameter = []    for single_square_feet ,single_price_value in zip(data[‘square_feet‘],data[‘price‘]):        X_parameter.append([float(single_square_feet)])        Y_parameter.append(float(single_price_value))    return X_parameter,Y_parameter# Function for Fitting our data to Linear modeldef linear_model_main(X_parameters,Y_parameters,predict_value):# Create linear regression object    regr = 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_outcome    return predictions# Function to show the resutls of linear fit modeldef show_linear_line(X_parameters,Y_parameters):# Create linear regression object    regr = 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))#----------------------------#----------------------------

     輸出的圖：

 

例子二：

 

代碼：

# -*- coding: utf-8 -*-‘‘‘Created on 2016/11/26 @author: chensi‘‘‘# Required Packagesimport csvimport sysimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom sklearn import datasets, linear_model # Function to get datadef get_data(file_name):    data = pd.read_excel(file_name)    flash_x_parameter = []    flash_y_parameter = []    arrow_x_parameter = []    arrow_y_parameter = []    for x1,y1,x2,y2 in zip(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.append([float(x2)])        arrow_y_parameter.append(float(y2))    return flash_x_parameter,flash_y_parameter,arrow_x_parameter,arrow_y_parameter # Function to know which Tv show will have more viewersdef more_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    if predicted_value1 > predicted_value2:        print ("The Flash Tv Show will have more viewers for next week")    else:        print ("Arrow Tv Show will have 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)

輸出：

 

python-[panda]-[sklearn]-[matplotlib]-線性預測