如何使用基于 csv 数据集的模型进行预测？

Question

按照教程，我做了一个神经网络，数据集来自我制作的csv文件。这是一个简单的数据集，其中包含每个学生的第一次考试成绩、第二次考试成绩、第三次考试成绩和国籍。目标是使用第一次和第二次考试结果和国籍来预测第三次考试结果。代码如下所示。

column_names = ['First exam result', 'Second exam result', 'Third exam result', 'Country']
dataset = pd.read_csv('data1.csv', names=column_names, sep=';')
dataset = dataset.dropna()  # clean data

# convert categorical 'Country' data into one-hot data
dataset.Country=pd.Categorical(dataset.Country, ['PL', 'ENG'], ordered=True)
dataset.Country=dataset.Country.cat.codes

# split data
train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

train_features = train_dataset.copy()
test_features = test_dataset.copy()

train_labels = train_features.pop('Third exam result')
test_labels = test_features.pop('Third exam result')

# Normalize
normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(train_features))

loss = keras.losses.MeanAbsoluteError()

linear_model = tf.keras.Sequential([
    normalizer,
    layers.Dense(64, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(units=1)])

linear_model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1), loss=loss)

linear_model.fit(
    train_features, train_labels,
    epochs=500,
    verbose=1,
    # Calculate validation results on 20% of the training data
    validation_split=0.2)

linear_model.evaluate(
    test_features, test_labels, verbose=1)

现在我想使用 testdata.csv 文件进行预测，其中包含除第三次考试结果之外的所有信息，但我不知道该怎么做。

prediction_data = pd.read_csv('testdata.csv', names=column_names, sep=';')

Answer 1

您需要对测试数据集做同样的操作

prediction_data.dropna(inplace=True)

prediction_data.Country=pd.Categorical(prediction_data.Country, ['PL', 'ENG'], ordered=True)
prediction_data.Country=prediction_data.Country.cat.codes
normalizer.adapt(np.array(prediction_data)) #You need normalize test data too

predict = linear_model.predict(prediction_data)

如何使用基于 csv 数据集的模型进行预测？

How to make a prediction using a model based on csv dataset?

python

machine-learning

prediction

keras

tensorflow