机器学习预测房价 利用机器学习预测房价

#导入类库和加载数据集
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

#读取数据
train_names = ["date",
               "price",
               "bedroom_num",
               "bathroom_num",
               "house_area",
               "park_space",
               "floor_num",
               "house_score",
               "covered_area",
               "basement_area",
               "yearbuilt",
               "yearremodadd",
               "lat",
               "long"]
data = pd.read_csv("kc_train.csv",names=train_names)
data.head()

# 观察数据集概况
data.info()

sell_year,sell_month,sell_day=[],[],[]
house_old,fix_old=[],[]
for [date,yearbuilt,yearremodadd] in data[['date','yearbuilt','yearremodadd']].values:
    year,month,day=date//10000,date%10000//100,date%100
    sell_year.append(year)
    sell_month.append(month)
    sell_day.append(day)
    house_old.append(year-yearbuilt)
    if yearremodadd==0:
        fix_old.append(0)
    else:
        fix_old.append(year-yearremodadd)
del data['date']
data['sell_year']=pd.DataFrame({'sell_year':sell_year})
data['sell_month']=pd.DataFrame({'sell_month':sell_month})
data['sell_day']=pd.DataFrame({'sell_day':sell_day})
data['house_old']=pd.DataFrame({'house_old':house_old})
data['fix_old']=pd.DataFrame({'fix_old':fix_old})
data.head()

#观察数据
print(data['price'].describe())

#观察price的数据分布
plt.figure(figsize = (10,5))
# plt.xlabel('price')
sns.distplot(data['price'])

#自变量与因变量的相关性分析
plt.figure(figsize = (20,10))
internal_chars = ['price','bedroom_num','bathroom_num','house_area','park_space','floor_num','house_score','covered_area'
                  ,'basement_area','yearbuilt','yearremodadd','lat','long','sell_year','sell_month','sell_day',
                 'house_old','fix_old']
corrmat = data[internal_chars].corr()  # 计算相关系数
sns.heatmap(corrmat, square=False, linewidths=.5, annot=True) #热力图
csdn.net/jlf7026/article/details/84630414

#打印出相关性的排名
print(corrmat["price"].sort_values(ascending=False))

#特征缩放
data = data.astype('float')
x = data.drop('price',axis=1)
y = data['price']
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
newX= scaler.fit_transform(x)
newX = pd.DataFrame(newX, columns=x.columns)
newX.head()

#先将数据集分成训练集和测试集
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(newX, y, test_size=0.2, random_state=21)

#模型建立
from sklearn import metrics
def RF(X_train, X_test, y_train, y_test):    #随机森林 
    from sklearn.ensemble import RandomForestRegressor
    model= RandomForestRegressor(n_estimators=200,max_features=None)
    model.fit(X_train, y_train)
    predicted= model.predict(X_test)
    mse = metrics.mean_squared_error(y_test,predicted)
    return (mse/10000)
def LR(X_train, X_test, y_train, y_test):    #线性回归
    from sklearn.linear_model import LinearRegression            
    LR = LinearRegression()
    LR.fit(X_train, y_train)
    predicted = LR.predict(X_test)
    mse = metrics.mean_squared_error(y_test,predicted)
    return (mse/10000)

print('RF mse: ',RF(X_train, X_test, y_train, y_test))
print('LR mse: ',LR(X_train, X_test, y_train, y_test))