解释代码

1.导入所需的库：导入需要使用的Python库，包括NumPy、Pandas、MXNet等。这些库提供了数据处理、机器学习和深度学习的功能。
```python
import numpy as np
import pandas as pd
from mxnet import autograd, init, gluon, nd
import mxnet
from mxnet.gluon import nn
from mxnet.gluon import data as gdata
from mxnet.gluon import loss as gloss
```


2.导入数据集：```python
train_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
```

3.
```python
train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]
```

4.
```python
all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))
```

5.
```python
numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
```

6.
```python
all_features[numeric_features] = all_features[numeric_features].apply(lambda x:(x - x.mean())/(x.std()))
all_features[numeric_features] = all_features[numeric_features].fillna(0)
```

7.
```python
all_features = pd.get_dummies(all_features, dummy_na=True)
all_features.shape
```

8.
```python
n_train = train_data.shape[0]
train_features = nd.array(all_features[:n_train].values)
test_features = nd.array(all_features[n_train:].values)
train_labels = nd.array(train_data.SalePrice.values).reshape((-1, 1))
```

9.
```python
loss = gloss.L2Loss()
model = nn.Sequential()
model.add(nn.Dense(8))
model.add(nn.Dropout(0.5))
model.add(nn.Dense(1))
model.initialize()
```

10.
```python
def log_rmse(model, features, labels):
    clipped_preds = nd.clip(model(features), 1, float('inf'))
    rmse = nd.sqrt(2 * loss(clipped_preds.log(), labels.log()).mean())
    return rmse.asscalar()
```

11.
```python
def train(model, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size):
    train_ls, test_ls = [], []
    train_iteration = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)
    trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': learning_rate, 'wd': weight_decay})
    for epoch in range(num_epochs):
        for X, y in train_iteration:
            with autograd.record():
                l = loss(model(X), y)
            l.backward()
            trainer.step(batch_size)
        train_ls.append(log_rmse(model, train_features, train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(model, test_features, test_labels))
    
    return train_ls, test_ls
```

12.
```python
def get_k_fold_data(k, i, X, y):
    assert k > 1
    fold_size = X.shape[0] // k
    X_train, y_train = None, None
    for j in range(k):
        idx = slice(j * fold_size, (j + 1) * fold_size)
        X_part, y_part = X[idx,:], y[idx]
        if j == i:
            X_valid, y_valid = X_part, y_part
        elif X_train is None:
            X_train, y_train = X_part, y_part
        else:
            X_train = nd.concat(X_train, X_part, dim=0)
            y_train = nd.concat(y_train, y_part, dim=0)
        
    return X_train, y_train, X_valid, y_valid
```

13.
```python
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size):
    train_l_sum, valid_l_sum = 0.0, 0.0
    for i in range(k):
        data = get_k_fold_data(k, i, X_train, y_train)
        train_ls, valid_ls = train(model, *data, num_epochs, learning_rate, weight_decay, batch_size)
        train_l_sum += train_ls[-1]
        valid_l_sum += valid_ls[-1]
        print("fold %d, train rmse: %.4f, valid rmse: %.4f" % (i, train_ls[-1], valid_ls[-1]))
    return train_l_sum / k, valid_l_sum / k
```

14.
```python
k, num_epochs, lr, weight_decay, batch_size = 20, 100, 2, 0, 64
```


15.
```python
train_l, valid_l, = k_fold(k, train_features, train_labels, num_epochs, lr, weight_decay, batch_size)
print("%d-fold validation: avg train rmse: %f, avg valid rmse：%f" % (k, train_l, valid_l))
```

16.
```python
preds = model(test_features).asnumpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis = 1)
submission.to_csv('submission.csv', index=False)
```