流程

1. 导入需要的包
2. 引入文件,查看内容
3. 数据处理
4. 调用模型准备训练
5. 输出结果

导入需要的包

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

引入文件，查看内容

train = pd.read_csv('train.csv')
print('The shape of training data:', train.shape)
train.head()

test = pd.read_csv('test.csv')
print('The shape of testing data:', test.shape)
test.head()

数据处理

删除没有用的列

train.drop('LotFrontage', axis=1, inplace=True)
test.drop('LotFrontage', axis=1, inplace=True)

区分数字特征和字符特征

#分离数字特征和类别特征
num_features = []
cate_features = []
for col in test.columns:
    if test[col].dtype == 'object':
        cate_features.append(col)
    else:
        num_features.append(col)
print('number of numeric features:', len(num_features))
print('number of categorical features:', len(cate_features))

去除特殊的值

#处理掉右下的明显异常值
train = train.drop(train[(train['TotalBsmtSF']>6000) & (train['SalePrice']<200000)].index)
train = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<200000)].index)

查看训练集中各特征的数据缺失个数

print('The shape of training data:', train.shape)
train_missing = train.isnull().sum()
train_missing = train_missing.drop(train_missing[train_missing==0].index).sort_values(ascending=False)
train_missing

查看测试集中各特征的数据缺失个数

#查看测试集中各特征的数据缺失个数
print('The shape of testing data:', test.shape)
test_missing = test.isnull().sum()
test_missing = test_missing.drop(test_missing[test_missing==0].index).sort_values(ascending=False)
test_missing

根据特征说明文档，以下特征缺失代表没有，所以直接补充为’None’就可以了：

none_lists = ['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'BsmtFinType1',
              'BsmtFinType2', 'BsmtCond', 'BsmtExposure', 'BsmtQual', 'MasVnrType']
for col in none_lists:
    train[col] = train[col].fillna('None')
    test[col] = test[col].fillna('None')

补充出现频率最高的一类

most_lists = ['MSZoning', 'Exterior1st', 'Exterior2nd', 'SaleType', 'KitchenQual', 'Electrical']
for col in most_lists:
    train[col] = train[col].fillna(train[col].mode()[0])
    test[col] = test[col].fillna(train[col].mode()[0])    #注意这里补充的是训练集中出现最多的类别

删除掉多余的特征

train['Functional'] = train['Functional'].fillna('Typ')
test['Functional'] = test['Functional'].fillna('Typ')

train.drop('Utilities', axis=1, inplace=True)
test.drop('Utilities', axis=1, inplace=True)

数字特征处理

补零，对可能为零的特征，缺失值全部补零

zero_lists = ['GarageYrBlt', 'MasVnrArea', 'BsmtFullBath', 'BsmtHalfBath', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'GarageCars', 'GarageArea',
              'TotalBsmtSF']
for col in zero_lists:
    train[col] = train[col].fillna(0)
    test[col] = test[col].fillna(0)

最后检查下是否还存在缺失值：

查看训练集是否有空

train.isnull().sum().any()

查看测试集是否有空

test.isnull().sum().any()

从存放类别特征的列表去掉

#从存放类别特征的列表去掉'Utilities'
cate_features.remove('Utilities')
print('The number of categorical features:', len(cate_features))

for col in cate_features:
    train[col] = train[col].astype(str)
    test[col] = test[col].astype(str)
le_features = ['Street', 'Alley', 'LotShape', 'LandContour', 'LandSlope', 'HouseStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'ExterQual', 
               'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'HeatingQC', 'CentralAir',
               'KitchenQual', 'Functional', 'FireplaceQu', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence']
for col in le_features:
    encoder = LabelEncoder()
    value_train = set(train[col].unique())
    value_test = set(test[col].unique())
    value_list = list(value_train | value_test)
    encoder.fit(value_list)
    train[col] = encoder.transform(train[col])
    test[col] = encoder.transform(test[col])

把数据放一块处理

all_data = pd.concat((train.drop('SalePrice', axis=1), test)).reset_index(drop=True)
all_data = pd.get_dummies(all_data, drop_first=True)  #注意独热编码生成的时候要去掉一个维度，保证剩下的变量都是相互独立的
all_data.shape

划分数据集

trainset = all_data[:1460]
y=train['SalePrice']
testset = all_data[1458:]
print('The shape of training data:', trainset.shape)
print('The shape of testing data:', testset.shape)

调用模型

linear_model = LinearRegression()
linear_model.fit(trainset, y)

预测数据

line_pre = linear_model.predict(testset)

输出结果

test = pd.read_csv('test.csv')
# print(test.shape,line_pre.shape)
we = pd.DataFrame({'Id': test['Id'], 'SalePrice': line_pre})
we.to_csv('House_Price_submissionMyself.csv', index=False)