roboreport

from sklearn.linear_model import LinearRegression


lm = LinearRegression(fit_intercept=True, normalize=True, n_jobs=None)
lm.fit(X_train, Y_train)
accuracy = lm.score(X_test, Y_test)
print "Linear Regression test file accuracy:"+str(accuracy)

lm.coef_

roboreport

X_Cols = X_train.rename(columns= {'region_cd': '지역코드(시도)', 'year': '연도', 'month':'월',  'building_type': '부동산타입',  
                               'tradeprice_sido' : '매매가격지수(시도)', 'construction_realized_amount' : '건설기성액(백만원)', "cd": "cd(91일물)",
                                'spirit_deposit_rate': '정기예금금리', 'exchange_rate': '환율', 'composite_stock_price_index': '종합주가지수',
                               'economy_growth': '경제성장률','exchequer_bond_three' : '국고채3년','household_loan_all': '가계대출액(전국)',
                               'mortgage_all' : '주택대출액(전국)', 'numberofnosells':'미분양 가구수(시도)','unsalenum_c':'공사완료후 미분양(민간,시도)' })

print(X_train.columns)
coefs = pd.DataFrame(zip(X_Cols.columns,lm.coef_), columns = ['features', 'coefficients'])

roboreport

%matplotlib inline
import matplotlib.pyplot as plt


Y_pred = lm.predict(X_test)

plt.scatter(Y_test, Y_pred)
plt.xlabel("Price Index: $Y_i$")
plt.ylabel("Predicted price Index: $\hat{Y}_i$")
plt.title("Prices vs Predicted price Index: $Y_i$ vs $\hat{Y}_i$")

roboreport

<?php


echo "\nstock cron start".date("Ymd")."\n"; 
$start=1;
$display=20;
$total=20;
$newname = "news.txt";
$line = "";
for($start=1;$start <=$total; $start=$start+$display)

roboreport

import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt

# 파일 업로드 기능 실행 
from google.colab import files
uploaded = files.upload() 

for fn in uploaded.keys():

roboreport

# 파일 업로드 기능 실행 
from google.colab import files
uploaded = files.upload() 

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(name=fn, length=len(uploaded[fn])))

roboreport

# 2017/1/1 까지의 데이터를 트레이닝셋.
# 그 이후 데이터를 테스트셋으로 한다.
split_date = pd.Timestamp('01-01-2017')


train = df.loc[:split_date, ['trade_price_idx_value']]
test = df.loc[split_date:, ['trade_price_idx_value']]

ax = train.plot()
test.plot(ax=ax)

roboreport

from sklearn.preprocessing import MinMaxScaler

sc = MinMaxScaler()

train_sc = sc.fit_transform(train)
test_sc = sc.transform(test)

train_sc

roboreport

train_sc_df = pd.DataFrame(train_sc, columns=['trade_price_idx_value'], index=train.index)
test_sc_df = pd.DataFrame(test_sc, columns=['trade_price_idx_value'], index=test.index)
train_sc_df.head()

roboreport

for s in range(1, 13):
    train_sc_df['shift_{}'.format(s)] = train_sc_df['trade_price_idx_value'].shift(s)
    test_sc_df['shift_{}'.format(s)] = test_sc_df['trade_price_idx_value'].shift(s)

train_sc_df.head(13)