kaggle教程--房屋价格预测代码

最新推荐文章于 2025-04-18 20:19:52 发布

大龄coder

最新推荐文章于 2025-04-18 20:19:52 发布

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分类专栏： kaggle 文章标签： kaggle 房屋价格 xgboost

本文链接：https://blog.youkuaiyun.com/weixin_42341153/article/details/91409889

版权

https://www.kaggle.com/c/home-data-for-ml-course

import pandas as pd
TRAIN_PATH = '/opt/work/jupyter/data/kaggle/house_price/train.csv'
TEST_PATH = '/opt/work/jupyter/data/kaggle/house_price/test.csv'

train_data = pd.read_csv(TRAIN_PATH)
test_data = pd.read_csv(TEST_PATH)

# 缺失数量大于100的列，大概可以去掉吧
missing_over_100_cols = [col for col in test_data.columns if test_data[col].isnull().sum() >= 100]

test_data = test_data.drop(missing_over_100_cols, axis=1)
train_data = train_data.drop(missing_over_100_cols, axis=1)


# 划分训练集和测试集
from sklearn.model_selection import train_test_split
y = train_data.SalePrice
X = train_data.drop(['SalePrice'], axis=1)
train_X, valid_X, train_y,valid_y = train_test_split(X,y,random_state=0)


# 填充缺失值,用最多的填充
from sklearn.impute import SimpleImputer
num