比赛给的数据集给与的是关于某个地区的房屋数据，要求运用机器学习的知识给出合理的房价预测

首先导入所需要的包

import pandas as pd
import numpy as np
import xgboost as xgb
import seaborn as sns
sns.set_style('whitegrid',{'font.sans-serif':['simhei','Arial']}) #这条是为了显示中文出现乱码错误
import matplotlib
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
from sklearn import linear_model, svm, gaussian_process
from sklearn.ensemble import RandomForestRegressor
from sklearn.cross_validation import train_test_split
import lightgbm as lgb
from sklearn import cross_validation, metrics
from sklearn.linear_model import LinearRegression

c:\program files\python36\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20."This module will be removed in 0.20.", DeprecationWarning)

train_data=pd.read_csv('./train.csv')
test_data=pd.read_csv('./test.csv')

train_data.head()
时间 小区名 小区房屋出租数量 楼层 总楼层 房屋面积 房屋朝向 居住状态 卧室数量 厅的数量 卫的数量 出租方式 区 位置 地铁线路 地铁站点 距离 装修情况 月租金
0 1 3072 0.128906 2 0.236364 0.008628 东南 NaN 1 1 1 NaN 11.0 118.0 2.0 40.0 0.764167 NaN 5.602716
1 1 3152 0.132812 1 0.381818 0.017046 东 NaN 1 0 0 NaN 10.0 100.0 4.0 58.0 0.709167 NaN 16.977929
2 1 5575 0.042969 0 0.290909 0.010593 东南 NaN 2 1 2 NaN 12.0 130.0 5.0 37.0 0.572500 NaN 8.998302
3 1 3103 0.085938 2 0.581818 0.019199 南 NaN 3 2 2 NaN 7.0 90.0 2.0 63.0 0.658333 NaN 5.602716
4 1 5182 0.214844 0 0.545455 0.010427 东北 NaN 2 1 1 NaN 3.0 31.0 NaN NaN NaN NaN 7.300509 
train_data.describe()
时间 小区名 小区房屋出租数量 楼层 总楼层 房屋面积 居住状态 卧室数量 厅的数量 卫的数量 出租方式 区 位置 地铁线路 地铁站点 距离 装修情况 月租金
count 196539.000000 196539.000000 195538.000000 196539.000000 196539.000000 196539.000000 20138.000000 196539.000000 196539.000000 196539.000000 24230.000000 196508.000000 196508.000000 91778.000000 91778.000000 91778.000000 18492.000000 196539.000000
mean 2.115229 3224.116562 0.124151 0.955449 0.408711 0.013139 2.725196 2.236635 1.299625 1.223818 0.900289 7.905139 67.945982 3.284850 57.493735 0.551202 3.589228 7.949313
std 0.786980 2023.073726 0.133299 0.851511 0.183100 0.008104 0.667763 0.896961 0.613169 0.487234 0.299621 4.025696 43.522394 1.477147 35.191414 0.247268 1.996912 6.310609
min 1.000000 0.000000 0.007812 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.001667 1.000000 0.000000
25% 1.000000 1388.000000 0.039062 0.000000 0.290909 0.009268 3.000000 2.000000 1.000000 1.000000 1.000000 4.000000 33.000000 2.000000 23.000000 0.356667 2.000000 4.923599
50% 2.000000 3086.000000 0.082031 1.000000 0.418182 0.012910 3.000000 2.000000 1.000000 1.000000 1.000000 9.000000 61.000000 4.000000 59.000000 0.554167 2.000000 6.621392
75% 3.000000 5199.000000 0.160156 2.000000 0.563636 0.014896 3.000000 3.000000 2.000000 1.000000 1.000000 11.000000 103.000000 5.000000 87.000000 0.745833 6.000000 8.998302
max 3.000000 6627.000000 1.000000 2.000000 1.000000 1.000000 3.000000 11.000000 8.000000 8.000000 1.000000 14.000000 152.000000 5.000000 119.000000 1.000000 6.000000 100.000000 
train_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 196539 entries, 0 to 196538
Data columns (total 19 columns):
时间          196539 non-null int64
小区名         196539 non-null int64
小区房屋出租数量    195538 non-null float64
楼层          196539 non-null int64
总楼层         196539 non-null float64
房屋面积        196539 non-null float64
房屋朝向        196539 non-null object
居住状态        20138 non-null float64
卧室数量        196539 non-null int64
厅的数量        196539 non-null int64
卫的数量        196539 non-null int64
出租方式        24230 non-null float64
区           196508 non-null float64
位置          196508 non-null float64
地铁线路        91778 non-null float64
地铁站点        91778 non-null float64
距离          91778 non-null float64
装修情况        18492 non-null float64
月租金         196539 non-null float64
dtypes: float64(12), int64(6), object(1)
memory usage: 28.5+ MB

corrmat = train_data.corr()
f, ax = plt.subplots(figsize=(20, 9))
sns.heatmap(corrmat, vmax=0.8, square=True)

<matplotlib.axes._subplots.AxesSubplot at 0x2949dbf46a0>


train_data=pd.read_csv('./train.csv')
test_data=pd.read_csv('./test.csv')
train_data=pd.DataFrame(train_data).fillna(0)
result=train_data['月租金'].values.reshape(-1,1)
del train_data['房屋朝向']
del test_data['房屋朝向']
# del train_data['月租金']
train_data=train_data.values
test_data=test_data.fillna(0).values
lr=LinearRegression()

lr.fit(train_data,result)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

train_data.shape

(196539, 18)

prediction=lr.predict(test_data)

prediction

array([[ 6.00000000e+00],[-3.05193784e-11],[-1.35930288e-11],...,[-5.85045654e-11],[-1.61906537e-10],[-1.27143911e-10]])

Del_columns = ['时间', '小区名', '小区房屋出租数量', '居住状态', '月租金', '装修情况']
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
for i in Del_columns:del train_data[i]if i!='月租金':del test_data[i]
del test_data['id']

train_data.head()
楼层 总楼层 房屋面积 房屋朝向 卧室数量 厅的数量 卫的数量 出租方式 区 位置 地铁线路 地铁站点 距离
0 2 0.236364 0.008628 东南 1 1 1 NaN 11.0 118.0 2.0 40.0 0.764167
1 1 0.381818 0.017046 东 1 0 0 NaN 10.0 100.0 4.0 58.0 0.709167
2 0 0.290909 0.010593 东南 2 1 2 NaN 12.0 130.0 5.0 37.0 0.572500
3 2 0.581818 0.019199 南 3 2 2 NaN 7.0 90.0 2.0 63.0 0.658333
4 0 0.545455 0.010427 东北 2 1 1 NaN 3.0 31.0 NaN NaN NaN 
test_data.head()
楼层 总楼层 房屋面积 房屋朝向 卧室数量 厅的数量 卫的数量 出租方式 区 位置 地铁线路 地铁站点 距离
0 1 0.600000 0.007117 东 2 1 1 1.0 10.0 5.0 NaN NaN NaN
1 1 0.472727 0.007448 东 2 1 1 NaN 3.0 0.0 NaN NaN NaN
2 2 0.709091 0.014068 东南 3 2 2 NaN 10.0 9.0 4.0 74.0 0.400833
3 0 0.090909 0.008937 南 2 1 1 NaN 6.0 96.0 5.0 17.0 0.384167
4 1 0.218182 0.008606 东南 2 1 1 NaN 6.0 61.0 3.0 114.0 0.598333 
def split_map(str):return str.strip().split(' ')

def check_bool(arr,str):bool_list=[]for i in arr:if str in i:bool_list.append(True)else:bool_list.append(False)return bool_list

def processData(data):temp=data['房屋朝向'].map(lambda x:split_map(x))data['东']=0data['南']=0data['西']=0data['北']=0data['东南']=0data['东北']=0data['西南']=0data['西北']=0bool_dong=check_bool(temp,'东')bool_nan=check_bool(temp,'南')bool_xi=check_bool(temp,'西')bool_bei=check_bool(temp,'北')bool_db=check_bool(temp,'东南')bool_dn=check_bool(temp,'东北')bool_xb=check_bool(temp,'西南')bool_xn=check_bool(temp,'西北')data.loc[bool_dong,'东']=1data.loc[bool_xi,'西']=1data.loc[bool_nan,'南']=1data.loc[bool_bei,'北']=1data.loc[bool_db,'东北']=1data.loc[bool_dn,'东南']=1data.loc[bool_xb,'西北']=1data.loc[bool_xn,'西南']=1del data['房屋朝向']return data

train_data=processData(train_data)

test_data=processData(test_data)

train_data.head()
楼层 总楼层 房屋面积 卧室数量 厅的数量 卫的数量 出租方式 区 位置 地铁线路 地铁站点 距离 东 南 西 北 东南 东北 西南 西北
0 2 0.236364 0.008628 1 1 1 NaN 11.0 118.0 2.0 40.0 0.764167 0 0 0 0 0 1 0 0
1 1 0.381818 0.017046 1 0 0 NaN 10.0 100.0 4.0 58.0 0.709167 1 0 0 0 0 0 0 0
2 0 0.290909 0.010593 2 1 2 NaN 12.0 130.0 5.0 37.0 0.572500 0 0 0 0 0 1 0 0
3 2 0.581818 0.019199 3 2 2 NaN 7.0 90.0 2.0 63.0 0.658333 0 1 0 0 0 0 0 0
4 0 0.545455 0.010427 2 1 1 NaN 3.0 31.0 NaN NaN NaN 0 0 0 0 1 0 0 0 
test_data.head()
楼层 总楼层 房屋面积 卧室数量 厅的数量 卫的数量 出租方式 区 位置 地铁线路 地铁站点 距离 东 南 西 北 东南 东北 西南 西北
0 1 0.600000 0.007117 2 1 1 1.0 10.0 5.0 NaN NaN NaN 1 0 0 0 0 0 0 0
1 1 0.472727 0.007448 2 1 1 NaN 3.0 0.0 NaN NaN NaN 1 0 0 0 0 0 0 0
2 2 0.709091 0.014068 3 2 2 NaN 10.0 9.0 4.0 74.0 0.400833 0 0 0 0 0 1 0 0
3 0 0.090909 0.008937 2 1 1 NaN 6.0 96.0 5.0 17.0 0.384167 0 1 0 0 0 0 0 0
4 1 0.218182 0.008606 2 1 1 NaN 6.0 61.0 3.0 114.0 0.598333 0 0 0 0 0 1 0 0 
train_data=train_data.fillna(0)
test_data=train_data.fillna(0)

train_data=train_data.values
test_data=test_data.values
train_result=pd.read_csv('train.csv')['月租金'].values

用xgbooster模型进行训练

# xbox
X_train, X_test, y_train, y_test = cross_validation.train_test_split(train_data, train_result, test_size=0.1,random_state=2333)xgb_val = xgb.DMatrix(X_test, label=y_test)
xgb_train = xgb.DMatrix(X_train, label=y_train)
xgb_test = xgb.DMatrix(test_data)
# xgbooster
params = {'booster': 'gbtree','objective': 'reg:linear',  # 多分类的问题'n_estimators': 2000,'gamma': 0.2,  # 用于控制是否后剪枝的参数,越大越保守，一般0.1、0.2这样子。'max_depth': 10,  # 构建树的深度，越大越容易过拟合"reg_alpha": 3,'lambda': 5,  # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。'subsample': 0.9,  # 随机采样训练样本'colsample_bytree': 0.6,  # 生成树时进行的列采样'colsample_bylevel': 0.7,'min_child_weight': 7,# 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言# ，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。# 这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。'silent': 1,  # 设置成1则没有运行信息输出，最好是设置为0.'eta': 0.05,  # 如同学习率  0.007'seed': 2017,}plst = list(params.items())
num_rounds = 10000  # 迭代次数
watchlist = [(xgb_train, 'train'), (xgb_val, 'val')]# 训练模型并保存
# early_stopping_rounds 当设置的迭代次数较大时，early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练
model = xgb.train(plst, xgb_train, num_rounds, watchlist, early_stopping_rounds=300, verbose_eval=50, )
model.save_model('xgb.model')  # 用于存储训练出的模型
print("模型训练完成")print("训练完毕，开始预测")
test_result = model.predict(xgb_test, ntree_limit=model.best_ntree_limit)data_df = pd.DataFrame(test_result)
filename = 'result_xgb.csv'
data_df.to_csv(filename, encoding='utf-8')

[0]  train-rmse:9.3703   val-rmse:9.49998
Multiple eval metrics have been passed: 'val-rmse' will be used for early stopping.Will train until val-rmse hasn't improved in 300 rounds.
[50]    train-rmse:2.77386  val-rmse:2.95125
[100]   train-rmse:2.22017  val-rmse:2.42123
[150]   train-rmse:2.05228  val-rmse:2.26014
[200]   train-rmse:1.95506  val-rmse:2.16923
[250]   train-rmse:1.87808  val-rmse:2.09895
[300]   train-rmse:1.81004  val-rmse:2.03828
[350]   train-rmse:1.75105  val-rmse:1.98529
[400]   train-rmse:1.69493  val-rmse:1.93967
[450]   train-rmse:1.64521  val-rmse:1.90085
[500]   train-rmse:1.59184  val-rmse:1.85845
[550]   train-rmse:1.54979  val-rmse:1.82564
[600]   train-rmse:1.51416  val-rmse:1.79824
[650]   train-rmse:1.476    val-rmse:1.76895
[700]   train-rmse:1.44457  val-rmse:1.74357
[750]   train-rmse:1.41372  val-rmse:1.72097
[800]   train-rmse:1.3867   val-rmse:1.69965
[850]   train-rmse:1.36016  val-rmse:1.67913
[900]   train-rmse:1.3367   val-rmse:1.6619
[950]   train-rmse:1.31308  val-rmse:1.64355
[1000]  train-rmse:1.2914   val-rmse:1.62736
[1050]  train-rmse:1.27203  val-rmse:1.61333
[1100]  train-rmse:1.2507   val-rmse:1.59796
[1150]  train-rmse:1.23164  val-rmse:1.58435
[1200]  train-rmse:1.21557  val-rmse:1.57225
[1250]  train-rmse:1.20111  val-rmse:1.56099
[1300]  train-rmse:1.18301  val-rmse:1.54762
[1350]  train-rmse:1.1699   val-rmse:1.53814
[1400]  train-rmse:1.15637  val-rmse:1.52793
[1450]  train-rmse:1.14323  val-rmse:1.51842
[1500]  train-rmse:1.13196  val-rmse:1.51052
[1550]  train-rmse:1.11936  val-rmse:1.50133
[1600]  train-rmse:1.10928  val-rmse:1.49404
[1650]  train-rmse:1.09593  val-rmse:1.48425
[1700]  train-rmse:1.08563  val-rmse:1.4771
[1750]  train-rmse:1.07649  val-rmse:1.47056
[1800]  train-rmse:1.06619  val-rmse:1.46278
[1850]  train-rmse:1.05751  val-rmse:1.45715
[1900]  train-rmse:1.04786  val-rmse:1.45038
[1950]  train-rmse:1.03764  val-rmse:1.44344
[2000]  train-rmse:1.02981  val-rmse:1.43775
[2050]  train-rmse:1.02199  val-rmse:1.43245
[2100]  train-rmse:1.01467  val-rmse:1.42699
[2150]  train-rmse:1.00615  val-rmse:1.42124
[2200]  train-rmse:0.997202 val-rmse:1.41497
[2250]  train-rmse:0.99015  val-rmse:1.41046
[2300]  train-rmse:0.98291  val-rmse:1.40615
[2350]  train-rmse:0.976352 val-rmse:1.40207
[2400]  train-rmse:0.969466 val-rmse:1.39734
[2450]  train-rmse:0.964016 val-rmse:1.39344
[2500]  train-rmse:0.958046 val-rmse:1.38977
[2550]  train-rmse:0.952188 val-rmse:1.38579
[2600]  train-rmse:0.945662 val-rmse:1.38188
[2650]  train-rmse:0.939815 val-rmse:1.37778
[2700]  train-rmse:0.934951 val-rmse:1.37506
[2750]  train-rmse:0.92956  val-rmse:1.37186
[2800]  train-rmse:0.925032 val-rmse:1.36926
[2850]  train-rmse:0.920306 val-rmse:1.36628
[2900]  train-rmse:0.915933 val-rmse:1.36362
[2950]  train-rmse:0.912063 val-rmse:1.36091
[3000]  train-rmse:0.907578 val-rmse:1.3581
[3050]  train-rmse:0.902738 val-rmse:1.35514
[3100]  train-rmse:0.899267 val-rmse:1.35305
[3150]  train-rmse:0.895539 val-rmse:1.35101
[3200]  train-rmse:0.892583 val-rmse:1.34929
[3250]  train-rmse:0.888493 val-rmse:1.3464
[3300]  train-rmse:0.885463 val-rmse:1.34454
[3350]  train-rmse:0.882028 val-rmse:1.34274
[3400]  train-rmse:0.87818  val-rmse:1.34027
[3450]  train-rmse:0.875323 val-rmse:1.33839
[3500]  train-rmse:0.871904 val-rmse:1.33632
[3550]  train-rmse:0.868129 val-rmse:1.33422
[3600]  train-rmse:0.865097 val-rmse:1.33224
[3650]  train-rmse:0.862143 val-rmse:1.33029
[3700]  train-rmse:0.8593   val-rmse:1.32906
[3750]  train-rmse:0.85648  val-rmse:1.32757
[3800]  train-rmse:0.854131 val-rmse:1.3263
[3850]  train-rmse:0.851365 val-rmse:1.32452
[3900]  train-rmse:0.848457 val-rmse:1.32238
[3950]  train-rmse:0.845943 val-rmse:1.32112
[4000]  train-rmse:0.843617 val-rmse:1.31984
[4050]  train-rmse:0.84074  val-rmse:1.3184
[4100]  train-rmse:0.838503 val-rmse:1.31692
[4150]  train-rmse:0.836258 val-rmse:1.31524
[4200]  train-rmse:0.83361  val-rmse:1.31377
[4250]  train-rmse:0.831158 val-rmse:1.31241
[4300]  train-rmse:0.828668 val-rmse:1.31086
[4350]  train-rmse:0.82635  val-rmse:1.30916
[4400]  train-rmse:0.824288 val-rmse:1.30784
[4450]  train-rmse:0.822547 val-rmse:1.30679
[4500]  train-rmse:0.820341 val-rmse:1.3061
[4550]  train-rmse:0.818466 val-rmse:1.30479
[4600]  train-rmse:0.816483 val-rmse:1.30372
[4650]  train-rmse:0.814547 val-rmse:1.30289
[4700]  train-rmse:0.812584 val-rmse:1.30213
[4750]  train-rmse:0.810988 val-rmse:1.30125
[4800]  train-rmse:0.809294 val-rmse:1.30042
[4850]  train-rmse:0.807515 val-rmse:1.29971
[4900]  train-rmse:0.805787 val-rmse:1.29873
[4950]  train-rmse:0.804336 val-rmse:1.29787
[5000]  train-rmse:0.802841 val-rmse:1.29688
[5050]  train-rmse:0.801239 val-rmse:1.29602
[5100]  train-rmse:0.799495 val-rmse:1.29525
[5150]  train-rmse:0.797696 val-rmse:1.29396
[5200]  train-rmse:0.796141 val-rmse:1.29346
[5250]  train-rmse:0.794911 val-rmse:1.29268
[5300]  train-rmse:0.793145 val-rmse:1.29126
[5350]  train-rmse:0.791934 val-rmse:1.29083
[5400]  train-rmse:0.790233 val-rmse:1.28989
[5450]  train-rmse:0.788694 val-rmse:1.28888
[5500]  train-rmse:0.786967 val-rmse:1.28807
[5550]  train-rmse:0.785497 val-rmse:1.28732
[5600]  train-rmse:0.784078 val-rmse:1.28612
[5650]  train-rmse:0.782551 val-rmse:1.28536
[5700]  train-rmse:0.781073 val-rmse:1.28481
[5750]  train-rmse:0.779708 val-rmse:1.28382
[5800]  train-rmse:0.778309 val-rmse:1.28302
[5850]  train-rmse:0.776918 val-rmse:1.28268
[5900]  train-rmse:0.775676 val-rmse:1.28189
[5950]  train-rmse:0.774513 val-rmse:1.28112
[6000]  train-rmse:0.773367 val-rmse:1.28045
[6050]  train-rmse:0.772218 val-rmse:1.27994
[6100]  train-rmse:0.771067 val-rmse:1.27933
[6150]  train-rmse:0.7696   val-rmse:1.27846
[6200]  train-rmse:0.768351 val-rmse:1.27754
[6250]  train-rmse:0.767365 val-rmse:1.27706
[6300]  train-rmse:0.766297 val-rmse:1.27632
[6350]  train-rmse:0.765223 val-rmse:1.27568
[6400]  train-rmse:0.764237 val-rmse:1.27524
[6450]  train-rmse:0.762684 val-rmse:1.2743
[6500]  train-rmse:0.761282 val-rmse:1.27319
[6550]  train-rmse:0.760169 val-rmse:1.27264
[6600]  train-rmse:0.759052 val-rmse:1.27181
[6650]  train-rmse:0.75798  val-rmse:1.2713
[6700]  train-rmse:0.756919 val-rmse:1.27098
[6750]  train-rmse:0.756039 val-rmse:1.27034---------------------------------------------------------------------------KeyboardInterrupt                         Traceback (most recent call last)<ipython-input-24-2313367f37bb> in <module>()34 # 训练模型并保存35 # early_stopping_rounds 当设置的迭代次数较大时，early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练
---> 36 model = xgb.train(plst, xgb_train, num_rounds, watchlist, early_stopping_rounds=300, verbose_eval=50, )37 model.save_model('xgb.model')  # 用于存储训练出的模型38 print("模型训练完成")c:\program files\python36\lib\site-packages\xgboost\training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)202                            evals=evals,203                            obj=obj, feval=feval,
--> 204                            xgb_model=xgb_model, callbacks=callbacks)205 206 c:\program files\python36\lib\site-packages\xgboost\training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)72         # Skip the first update if it is a recovery step.73         if version % 2 == 0:
---> 74             bst.update(dtrain, i, obj)75             bst.save_rabit_checkpoint()76             version += 1c:\program files\python36\lib\site-packages\xgboost\core.py in update(self, dtrain, iteration, fobj)1019         if fobj is None:1020             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),
-> 1021                                                     dtrain.handle))1022         else:1023             pred = self.predict(dtrain)KeyboardInterrupt:
  • 2月12日 补充
    在房屋朝向方面，可以运用更简单的方法来进行处理：

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