kaggle房价预测特征意思_Kaggle初探--房价预测案例之数据分析

概述

在做的过程中，浏览了好多出色的报告，受益匪浅，浏览的文章主要包括:

import pandas as pd

import numpy as np

import seaborn as sns

from scipy import stats

from scipy.stats import skew

from scipy.stats import norm

import matplotlib.pyplot as plt

from sklearn.preprocessing import StandardScaler

from sklearn.manifold import TSNE

from sklearn.cluster import KMeans

from sklearn.decomposition import PCA

from sklearn.preprocessing import StandardScaler

# import warnings

# warnings.filterwarnings('ignore')

%config InlineBackend.figure_format = 'retina' #set 'png' here when working on notebook

%matplotlib inline

train_df = pd.read_csv("../input/train.csv")

test_df = pd.read_csv("../input/test.csv")

查看数据

我们拿到数据后，先对数据要有个大致的了解，我们有1460的训练数据和1460的测试数据，数据的特征列有81个，其中35个是数值类型的，44个类别类型。

我们通过阅读数据的描述说明，会发现列MSSubClass,OverallQual,OverallCond 这些数据可以将其转换为类别类型.

但是去具体看OverallQual,OverallCond 的时候，其没有缺失列，可以当做int来处理

all_df = pd.concat((train_df.loc[:,'MSSubClass':'SaleCondition'], test_df.loc[:,'MSSubClass':'SaleCondition']), axis=0,ignore_index=True)

all_df['MSSubClass'] = all_df['MSSubClass'].astype(str)

quantitative = [f for f in all_df.columns if all_df.dtypes[f] != 'object']

qualitative = [f for f in all_df.columns if all_df.dtypes[f] == 'object']

print("quantitative: {}, qualitative: {}" .format (len(quantitative),len(qualitative)))

quantitative: 35, qualitative: 44

处理缺失数据

对于缺失值的处理

缺失的行特别对，弃用该列

缺失的值比较少，取均值

缺失的值中间，对于类别信息的列可以将缺失作为新的类别做 one-hot

missing = all_df.isnull().sum()

missing.sort_values(inplace=True,ascending=False)

missing = missing[missing > 0]

types = all_df[missing.index].dtypes

percent = (all_df[missing.index].isnull().sum()/all_df[missing.index].isnull().count()).sort_values(ascending=False)

missing_data = pd.concat([missing, percent,types], axis=1, keys=['Total', 'Percent','Types'])

missing_data.sort_values('Total',ascending=False,inplace=True)

missing_data

image.png

missing.plot.bar()

output_14_1.png

上述缺失的列中有6列大于了15%的缺失率，其余主要是 BsmtX 和 GarageX 两大类，我们在具体决定这些列的处理之前，我们来看下我们要预测的价格的一些特征

数据统计分析

单变量分析

先看下我们要预测的价格的一些统计信息

train_df.describe()['SalePrice']

count 1460.000000

mean 180921.195890

std 79442.502883

min 34900.000000

25% 129975.000000

50% 163000.000000

75% 214000.000000

max 755000.000000

Name: SalePrice, dtype: float64

#skewness and kurtosis

print("Skewness: %f" % train_df['SalePrice'].skew())

print("Kurtosis: %f" % train_df['SalePrice'].kurt())

# 在统计学中，峰度(Kurtosis)衡量实数随机变量概率分布的峰态。峰度高就意味着方差增大是由低频度的大于或小于平均值的极端差值引起的。

Skewness: 1.882876

Kurtosis: 6.536282

相关性

我们先通过计算变量相关性，大致看下最相关的列都有什么

corrmat = train_df.corr()

#saleprice correlation matrix

k = 10 #number of variables for heatmap

cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index

cm = np.corrcoef(train_df[cols].values.T)

sns.set(font_scale=1.25)

hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)

plt.show()

output_21_0.png

## 同时是相关性列，也是缺失数据的

missing_data.index.intersection(cols)

Index(['GarageCars', 'GarageArea', 'TotalBsmtSF'], dtype='object')

missing_data.loc[missing_data.index.intersection(cols)]

image.png

从上面最相关的图中，我们可以首先将缺失的数据都给删除的

#dealing with missing data

all_df = all_df.drop((missing_data[missing_data['Total'] > 1]).index,1)

# df_train = df_train.drop(df_train.loc[df_train['Electrical'].isnull()].index)

all_df.isnull().sum().max() #just checking that there's no missing data missing...

# 对于missing 1 的我们到时候已平均数填充

#histogram and normal probability plot

sns.distplot(train_df['SalePrice'], fit=norm);

fig = plt.figure()

res = stats.probplot(train_df['SalePrice'], plot=plt)

output_27_0.png

output_27_1.png

一个好的处理方法就是进行log

train_df['SalePrice'] = np.log(train_df['SalePrice'])

#histogram and normal probability plot

sns.distplot(train_df['SalePrice'], fit=norm);

fig = plt.figure()

res = stats.probplot(train_df['SalePrice'], plot=plt)

output_30_0.png

output_30_1.png

看下每个定量变量的分布图

quantitative = [f for f in all_df.columns if all_df.dtypes[f] != 'object']

qualitative = [f for f in all_df.columns if all_df.dtypes[f] == 'object']

print("quantitative: {}, qualitative: {}" .format (len(quantitative),len(qualitative)))

quantitative: 30, qualitative: 26

f = pd.melt(all_df, value_vars=quantitative)

g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False)

g = g.map(sns.distplot, "value")

output_33_0.png

上面有些数据是类似于正态分布的，我们可以对其进行log操作了提升质量的，有些则不适合，合适的预选对象有LotArea,BsmtUnfSF,1stFlrSF,TotalBsmtSF,KitchenAbvGr

我们计算下我们定量数据的偏度

all_df[quantitative].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)

MiscVal 21.947195

PoolArea 16.898328

LotArea 12.822431

LowQualFinSF 12.088761

3SsnPorch 11.376065

KitchenAbvGr 4.302254

BsmtFinSF2 4.145323

EnclosedPorch 4.003891

ScreenPorch 3.946694

OpenPorchSF 2.535114

WoodDeckSF 1.842433

1stFlrSF 1.469604

BsmtFinSF1 1.424989

GrLivArea 1.269358

TotalBsmtSF 1.162285

BsmtUnfSF 0.919351

2ndFlrSF 0.861675

TotRmsAbvGrd 0.758367

Fireplaces 0.733495

HalfBath 0.694566

OverallCond 0.570312

BedroomAbvGr 0.326324

GarageArea 0.241176

OverallQual 0.197110

MoSold 0.195884

FullBath 0.167606

YrSold 0.132399

GarageCars -0.218260

YearRemodAdd -0.451020

YearBuilt -0.599806

dtype: float64

定量特征分析

方差分析或变方分析(Analysis of variance，简称 ANOVA)为数据分析中常见的统计模型

train = all_df.loc[train_df.index]

train['SalePrice'] = train_df.SalePrice

def anova(frame):

anv = pd.DataFrame()

anv['feature'] = qualitative

pvals = []

for c in qualitative:

samples = []

for cls in frame[c].unique():

s = frame[frame[c] == cls]['SalePrice'].values

samples.append(s)

pval = stats.f_oneway(*samples)[1]

pvals.append(pval)

anv['pval'] = pvals

return anv.sort_values('pval')

a = anova(train)

a['disparity'] = np.log(1./a['pval'].values)

sns.barplot(data=a, x='feature', y='disparity')

x=plt.xticks(rotation=90)

/Users/zhuanxu/anaconda/envs/linear_regression_demo/lib/python3.6/site-packages/scipy/stats/stats.py:2958: RuntimeWarning: invalid value encountered in double_scalars

ssbn += _square_of_sums(a - offset) / float(len(a))

output_38_1.png

此处 stats.f_oneway 的作用是计算这种定性变量对于SalePrice的作用，如果GarageType的每个类别SalePrice的价格方差差不多，意味着该变量对于SalePrice就没什么作用，stats.f_oneway 返回的 pval > 0.05，基本就意味着量集合的相似，具体可以看

下面对这些定性变量进行下处理，对齐进行数值编码，让他转换为定性的列

def encode(frame, feature):

ordering = pd.DataFrame()

ordering['val'] = frame[feature].unique()

ordering.index = ordering.val

ordering['spmean'] = frame[[feature, 'SalePrice']].groupby(feature).mean()['SalePrice']

ordering = ordering.sort_values('spmean')

ordering['ordering'] = range(1, ordering.shape[0]+1)

ordering = ordering['ordering'].to_dict()

for cat, o in ordering.items():

frame.loc[frame[feature] == cat, feature+'_E'] = o

qual_encoded = []

for q in qualitative:

encode(train, q)

qual_encoded.append(q+'_E')

print(qual_encoded)

['MSSubClass_E', 'Street_E', 'LotShape_E', 'LandContour_E', 'LotConfig_E', 'LandSlope_E', 'Neighborhood_E', 'Condition1_E', 'Condition2_E', 'BldgType_E', 'HouseStyle_E', 'RoofStyle_E', 'RoofMatl_E', 'Exterior1st_E', 'Exterior2nd_E', 'ExterQual_E', 'ExterCond_E', 'Foundation_E', 'Heating_E', 'HeatingQC_E', 'CentralAir_E', 'Electrical_E', 'KitchenQual_E', 'PavedDrive_E', 'SaleType_E', 'SaleCondition_E']

# 选出了包含缺失数据的行，处理一下

missing_data = all_df.isnull().sum()

missing_data = missing_data[missing_data>0]

ids = all_df[missing_data.index].isnull()

# index (0), columns (1)

all_df.loc[ids[ids.any(axis=1)].index][missing_data.index]

image.png

# 处理完后对于nan的数据，其值还是nan

train.loc[1379,'Electrical_E']

nan

相关性计算

def spearman(frame, features):

spr = pd.DataFrame()

spr['feature'] = features

#Signature: a.corr(other, method='pearson', min_periods=None)

#Docstring:

#Compute correlation with `other` Series, excluding missing values

# 计算特征和 SalePrice的 斯皮尔曼 相关系数

spr['spearman'] = [frame[f].corr(frame['SalePrice'], 'spearman') for f in features]

spr = spr.sort_values('spearman')

plt.figure(figsize=(6, 0.25*len(features))) # width, height

sns.barplot(data=spr, y='feature', x='spearman', orient='h')

features = quantitative + qual_encoded

spearman(train, features)

output_45_0.png

从上图我们可以看到特征 OverallQual Neighborhood GrLiveArea 对价格影响都比较大

下面我们分析下特征列之间的相关性，如果两特征相关，在做回归的时候会导致共线性问题

plt.figure(1)

corr = train[quantitative+['SalePrice']].corr()

sns.heatmap(corr)

plt.figure(2)

corr = train[qual_encoded+['SalePrice']].corr()

sns.heatmap(corr)

plt.figure(3)

# [31,27]

corr = pd.DataFrame(np.zeros([len(quantitative)+1, len(qual_encoded)+1]), index=quantitative+['SalePrice'], columns=qual_encoded+['SalePrice'])

for q1 in quantitative+['SalePrice']:

for q2 in qual_encoded+['SalePrice']:

corr.loc[q1, q2] = train[q1].corr(train[q2])

sns.heatmap(corr)

output_47_1.png

output_47_2.png

output_47_3.png

Pairplots

def pairplot(x, y, **kwargs):

ax = plt.gca()

ts = pd.DataFrame({'time': x, 'val': y})

ts = ts.groupby('time').mean()

ts.plot(ax=ax)

plt.xticks(rotation=90)

f = pd.melt(train, id_vars=['SalePrice'], value_vars=quantitative+qual_encoded)

g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False, size=5)

g = g.map(pairplot, "value", "SalePrice")

IOPub data rate exceeded.

The notebook server will temporarily stop sending output

to the client in order to avoid crashing it.

To change this limit, set the config variable

`--NotebookApp.iopub_data_rate_limit`.

从上面的数据我们能清晰的看到哪些变量是线性关系比较好的，哪些是非线性关系，还有一些能看到如果加二次项可能会表现出比较的线性相关性出来

价格分段

我们对于价格简单的做一个二分，然后看下特征的不同，我们先看下SalePrice的图

a = train['SalePrice']

a.plot.hist()

output_51_1.png

features = quantitative

standard = train[train['SalePrice'] < np.log(200000)]

pricey = train[train['SalePrice'] >= np.log(200000)]

diff = pd.DataFrame()

diff['feature'] = features

diff['difference'] = [(pricey[f].fillna(0.).mean() - standard[f].fillna(0.).mean())/(standard[f].fillna(0.).mean())

for f in features]

sns.barplot(data=diff, x='feature', y='difference')

x=plt.xticks(rotation=90)

![Uploading output_52_0_342062.png . . .]

上图可以看到贵的房子，泳池会影响比较大

分类

我们先对数据做一个简单的分类

features = quantitative + qual_encoded

model = TSNE(n_components=2, random_state=0, perplexity=50)

X = train[features].fillna(0.).values

tsne = model.fit_transform(X)

std = StandardScaler()

s = std.fit_transform(X)

pca = PCA(n_components=30)

pca.fit(s)

pc = pca.transform(s)

kmeans = KMeans(n_clusters=5)

kmeans.fit(pc)

fr = pd.DataFrame({'tsne1': tsne[:,0], 'tsne2': tsne[:, 1], 'cluster': kmeans.labels_})

sns.lmplot(data=fr, x='tsne1', y='tsne2', hue='cluster', fit_reg=False)

print(np.sum(pca.explained_variance_ratio_))

0.838557886152

output_55_1.png

30个成分能覆盖83%的方差，整体看来，这种聚类方法不太好

总结

本文对数据进行了一些分析，下一篇会基于这个分析做模型处理