Python 如何生成带有特定条目的Kaggle提交CSV文件？
我是机器学习的初学者，我正试图通过卡格尔的巨大问题来学习。我已经完成了我的代码，并且获得了0.78的准确分数，但是现在我需要生成一个CSV文件，其中包含418个条目+一个标题行，但是idk知道如何操作 这是我应该制作的一个例子：Python 如何生成带有特定条目的Kaggle提交CSV文件？,python,pandas,machine-learning,scikit-learn,kaggle,Python,Pandas,Machine Learning,Scikit Learn,Kaggle,我是机器学习的初学者，我正试图通过卡格尔的巨大问题来学习。我已经完成了我的代码，并且获得了0.78的准确分数，但是现在我需要生成一个CSV文件，其中包含418个条目+一个标题行，但是idk知道如何操作 这是我应该制作的一个例子： PassengerId,Survived 892,0 893,1 894,0 Etc. 数据来自我的test\u预测 这是我的代码： import pandas as pd from sklearn.tree import DecisionTreeClassi
PassengerId,Survived
892,0
893,1
894,0
Etc.
test\u预测import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
"""Assigning the train & test datasets' adresses to variables"""
train_path = "C:\\Users\\Omar\\Downloads\\Titanic Data\\train.csv"
test_path = "C:\\Users\\Omar\\Downloads\\Titanic Data\\test.csv"
"""Using pandas' read_csv() function to read the datasets
and then assigning them to their own variables"""
train_data = pd.read_csv(train_path)
test_data = pd.read_csv(test_path)
"""Using pandas' factorize() function to represent genders (male/female)
with binary values (0/1)"""
train_data['Sex'] = pd.factorize(train_data.Sex)[0]
test_data['Sex'] = pd.factorize(test_data.Sex)[0]
"""Replacing missing values in the training and test dataset with 0"""
train_data.fillna(0.0, inplace = True)
test_data.fillna(0.0, inplace = True)
"""Selecting features for training"""
columns_of_interest = ['Pclass', 'Sex', 'Age']
"""Dropping missing/NaN values from the training dataset"""
filtered_titanic_data = train_data.dropna(axis=0)
"""Using the predictory features in the data to handle the x axis"""
x = filtered_titanic_data[columns_of_interest]
"""The survival (what we're trying to find) is the y axis"""
y = filtered_titanic_data.Survived
"""Splitting the train data with test"""
train_x, val_x, train_y, val_y = train_test_split(x, y, random_state=0)
"""Assigning the DecisionClassifier model to a variable"""
titanic_model = DecisionTreeClassifier()
"""Fitting the x and y values with the model"""
titanic_model.fit(train_x, train_y)
"""Predicting the x-axis"""
val_predictions = titanic_model.predict(val_x)
"""Assigning the feature columns from the test to a variable"""
test_x = test_data[columns_of_interest]
"""Predicting the test by feeding its x axis into the model"""
test_predictions = titanic_model.predict(test_x)
"""Printing the prediction"""
print(val_predictions)
"""Checking for the accuracy"""
print(accuracy_score(val_y, val_predictions))
"""Printing the test prediction"""
print(test_predictions)
submission = pd.DataFrame({ 'PassengerId': test_data.passengerid.values, 'Survived': test_predictions })
submission.to_csv("my_submission.csv", index=False)
submission = pd.DataFrame({ 'PassengerId': test_data.passengerid.values, 'Survived': test_predictions })
submission.to_csv("my_submission.csv", index=False)
问题是什么？你的解决方案有什么不足之处？它做什么或不做什么是不正确的？您是否遇到错误/异常？
如何使用Python生成带有特定条目的CSV文件？提交['surved']=test\u预测submission.to_csv（'filename.csv'，index=False）如何使用Python生成带有特定条目的CSV文件？提交['surved']=test\u预测测试数据.passengerid.values[：418]测试预测[：418]test\u data.passengerid.values[:418]test\u预测[:418]