python 线性回归 预测数据

忙碌的一周将至尾声

本周尝试线性回归预测房价
假设方程式
y=kx+b
数据集：

代码如下：

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import datasets,linear_model

def get_data(filename):
    data = pd.read_csv(filename)
    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_sprice_value))
    return x_parameter,y_parameter

def linear_model_main(x_parameter,y_parameter,predict_value):
    regr = linear_model.LinearRegression()
    regr.fit(x_parameter,y_parameter)
    predict_outcome = regr.predict(predict_value)
    predicitions={}
    predictions['intercept']=regr.intercept_
    predictions['coefficient']=regr.coef_
    predictions['predicted_value']=predict_outcome_
    return predictions

def show_linear_line(x_parameter,y_parameter):
    regr=linear_model.LinearRegression()
    regr.fit(x_parameter,y_parameter)
    plt.scatter(x_parameter,y_parameter,color='blue')
    plt.plot(x_parameterregr.predict(x_parameter),color='red',linewidth=4)
    plt.xticks()
    plt.yticks()
    plt.show()

x,y=get_data('house_price.csv')
predictvalue=500
result=linear_model_main(x,y,predictvalue)
print('intercept value', result['intercept'])
print('coefficient value', result['coefficient'])
print('predicted value', result['predicted_value'])

show_linear_line(x,y)