八大直播，八大案例_第四讲

要求如下：

研究二手房价的影响因素，建立房价预测模型，数据存放在“sndHsPr.csv”中。

分析思路：

在对房价的影响因素进行模型研究之前，首先对各变量进行描述性分析，以初步判断房价的影响因素，进而建立房价预测模型

变量说明如下：

dist-所在区

roomnum-室的数量

halls-厅的数量

AREA-房屋面积

floor-楼层

subway-是否临近地铁

school-是否学区房

price-平米单价

步骤如下：

（一） 因变量分析：单位面积房价分析

（二） 自变量分析：

   2.1 自变量自身分布分析

   2.2 自变量对因变量影响分析

（三）建立房价预测模型

   3.1 线性回归模型

   3.2  对因变量取对数的线性模型

   3.3  考虑交互项的对数线性

（四）预测： 假设有一家三口，父母为了能让孩子在东城区上学，想买一套邻近地铁的两居室，面积是70平方米，中层楼层，那么房价大约是多少呢？

# coding: utf-8



# In[73]:



import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import matplotlib

import seaborn as sns

import scipy.stats as stats

from statsmodels.formula.api import ols





# In[2]:



data = pd.read_csv(r'E:\数据分析\天善-PYTHON\八场直播，八大案例（金融风控）\第4讲\提交-第四讲：统计建模与分析报告-二手房价格分析报告\作业\sndHsPr.csv',encoding = 'gbk')

data.head()





# In[4]:



#单位面积房价分析

data.price.describe()





# In[14]:



#自变量自身分布分析

data1 = ['dist','roomnum','halls','floor','subway','school']

data2 = ['AREA']

for i in data1:

    print('name：',i)

    print(data[i].value_counts())

    data[i].value_counts().plot(kind = 'bar')

    plt.show()

    

for i in data2:

    print('name:',i)

    print(data[i].describe())

    sns.distplot(data[i],kde=True,fit=stats.norm)

    fig = sm.qqplot(data[i],fit=True,line='45')

    fig.show()





# In[65]:



#自变量自身分布分析

#看变量有哪些类型

#data.dtypes

for i in data.columns.values:

    if data[i].dtypes == 'float64':

        plt.boxplot(x = data[i])

        plt.title(i)

        plt.show()

    else:

        data[i].value_counts().plot(kind = 'bar')

        plt.title(i)

        plt.show()





# In[70]:



#自变量对因变量影响分析

#1.X、Y都是连续变量

var1 = ['roomnum','halls','AREA']

for i in var1:

    print(data[[i,'price']].corr(method = 'pearson'))

#2.X是分类变量

var2 = ['subway','school']

for i in var2:

    print(data[[i,'price']].corr(method = 'spearman'))





# In[75]:



#建立房价预测模型

lm_s = ols('price~roomnum+halls+AREA+subway+school',data = data).fit()

lm_s.summary()





# In[76]:



'''forward select'''

def forward_select(data, response):

    remaining = set(data.columns)

    remaining.remove(response)

    selected = []

    current_score, best_new_score = float('inf'), float('inf')

    while remaining:

        aic_with_candidates=[]

        for candidate in remaining:

            formula = "{} ~ {}".format(

                response,' + '.join(selected + [candidate]))

            aic = ols(formula=formula, data=data).fit().aic

            aic_with_candidates.append((aic, candidate))

        aic_with_candidates.sort(reverse=True)

        best_new_score, best_candidate=aic_with_candidates.pop()

        if current_score > best_new_score: 

            remaining.remove(best_candidate)

            selected.append(best_candidate)

            current_score = best_new_score

            print ('aic is {},continuing!'.format(current_score))

        else:        

            print ('forward selection over!')

            break

            

    formula = "{} ~ {} ".format(response,' + '.join(selected))

    print('final formula is {}'.format(formula))

    model = ols(formula=formula, data=data).fit()

    return(model)





# In[81]:



data_for_select = data[['price','roomnum','halls','AREA','subway','school']]

lm_m = forward_select(data = data_for_select,response = 'price')





# In[86]:



#对因变量取对数的线性模型

data['ln_price'] =np.log(data['price'])

lm_s2 = ols('ln_price~roomnum+halls+AREA+subway+school',data = data).fit()

lm_s2.summary()

data_for_select2 = data[['ln_price','roomnum','halls','AREA','subway','school']]

lm_m2 = forward_select(data_for_select2,response = 'ln_price')





# In[92]:



#预测：假设有一家三口，父母为了能让孩子在东城区上学，想买一套邻近地铁的两居室，面积是70平方米，中层楼层，那么房价大约是多少呢？

predict_data=[{"dist":"东城区","roomnum":2,"halls":0,"floor":"middle",

      "subway":1,"school":1,"AREA":70,"price":0,"ln_price":0}]

predict_data_final = pd.DataFrame(predict_data)

lm_s2.predict(predict_data_final)