import pandas as pd

data = pd.read_csv('house_price.csv')

print("数据集中的前10条记录:")

print(data.head(10))

print("")

# 数据进行可视化

from matplotlib import pyplot as plt

fig = plt.figure(figsize=(20,5))

fig1 = plt.subplot(131)

plt.scatter(data.loc[:,'面积'],data.loc[:,'价格'])

plt.title('Price VS Size')

fig2 = plt.subplot(132)

plt.scatter(data.loc[:,'人均收入'],data.loc[:,'价格'])

plt.title('Price VS Income')

fig3 = plt.subplot(133)

plt.scatter(data.loc[:,'平均房龄'],data.loc[:,'价格'])

plt.title('Price VS House_age')

plt.show()

#数据预处理

import numpy as np

X = data.drop(['价格'],axis=1)

y = data.loc[:,'价格']

X = np.array(X)

y = np.array(y)

print(X.shape,y.shape)

y = y.reshape(-1,1)

print(X.shape,y.shape)

#建立多因子回归模型 并且训练

from sklearn.linear_model import LinearRegression

model_multi = LinearRegression()

model_multi.fit(X,y)

#房价预测

X_test = np.array([[150,60000,5]])

y_test_predict = model_multi.predict(X_test)

print(y_test_predict)