### 机器学习代写|自然语言处理代写NLP代考|Working with Data

statistics-lab™ 为您的留学生涯保驾护航 在代写自然语言处理NLP方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写自然语言处理NLP代写方面经验极为丰富，各种代写自然语言处理NLP相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 机器学习代写|自然语言处理代写NLP代考|WHAT ARE DATASETS

In simple terms, a dataset is a source of data (such as a text file) that contains rows and columns of data. Each row is typically called a “data point,” and each column is called a “feature.” A dataset can be in any form: CSV (comma separated values), TSV (tab separated values), Excel spreadsheet, a table in an RDMBS (Relational Database Management System), a document

in a NoSQL database, or the output from a Web service. Someone needs to analyze the dataset to determine which features are the most important and which features can be safely ignored in order to train a model with the given dataset.

A dataset can vary from very small (a couple of features and 100 rows) to very large (more than 1,000 features and more than one million rows). If you are unfamiliar with the problem domain, then you might struggle to determine the most important features in a large dataset. In this situation, you might need a domain expert who understands the importance of the features, their interdependencies (if any), and whether the data values for the features are valid. In addition, there are algorithms (called dimensionality reduction algorithms) that can help you determine the most important features. For example, PCA (Principal Component Analysis) is one such algorithm, which is discussed in more detail later in this chapter.

## 机器学习代写|自然语言处理代写NLP代考|Data Preprocessing

Data preprocessing is the initial step that involves validating the contents of a dataset, which involves making decisions about missing and incorrect data values such as

• dealing with missing data values
• cleaning “noisy” text-based data
• removing HTML tags
• removing emoticons
• dealing with emojis/emoticons
• filtering data
• grouping data
• handling currency and date formats (il8n)
Cleaning data is an important initial task that involves removing unwanted data as well as handling missing data. In the case of text-based data, you might need to remove HTML tags, punctuation, and so forth. In the case of numeric data, it’s less likely (though still possible) that alphabetic characters are mixed together with numeric data. However, a dataset with numeric features might have incorrect values or missing values (discussed later). In addition, calculating the minimum, maximum, mean, median, and standard deviation of the values of a feature obviously pertain only to numeric values.
After the preprocessing step is completed, data wrangling is performed, which refers to transforming data into a new format. You might have to combine data from multiple sources into a single dataset. For example, you might

need to convert between different units of measurement (such as date formats or currency values) so that the data values can be represented in a consistent manner in a dataset.

Currency and date values are part of $i 18 n$ (internationalization), whereas l10n (localization) targets a specific nationality, language, or region. Hardcoded values (such as text strings) can be stored as resource strings in a file that’s often called a resource bundle, where each string is referenced via a code. Each language has its own resource bundle.

## 机器学习代写|自然语言处理代写NLP代考|DATA TYPES

Explicit data types exist in many programming languages such as $\mathrm{C}, \mathrm{C}++$, Java, and TypeScript. Some programming languages, such as JavaScript and awk, do not require initializing variables with an explicit type: the type of a variable is inferred dynamically via an implicit type system (i.e., one that is not directly exposed to a developer).

In machine learning, datasets can contain features that have different types of data, such as a combination of one or more of the following:

• numeric data (integer/floating point and discrete/continuous)
• character/categorical data (different languages)
• date-related data (different formats)
• currency data (different formats)
• binary data (yes/no, 0/1, and so forth)
• nominal data (multiple unrelated values)
• ordinal data (multiple and related values)
Consider a dataset that contains real estate data, which can have as many as thirty columns (or even more), often with the following features:
• the number of bedrooms in a house: numeric value and a discrete value
• the number of square feet: a numeric value and (probably) a continuous value
• the name of the city: character data
• the construction date: a date value
• the selling price: a currency value and probably a continuous value
• the “for sale” status: binary data (either “yes” or “no”)
An example of nominal data is the seasons in a year: although many countries have four distinct seasons, some countries have only two distinct seasons.

## 机器学习代写|自然语言处理代写NLP代考|Data Preprocessing

• 处理缺失的数据值
• 清理“嘈杂”的基于文本的数据
• 删除 HTML 标签
• 删除表情符号
• 处理表情符号/表情符号
• 过滤数据
• 分组数据
• 处理货币和日期格式 (il8n)
清理数据是一项重要的初始任务，包括删除不需要的数据以及处理丢失的数据。对于基于文本的数据，您可能需要删除 HTML 标记、标点符号等。在数字数据的情况下，字母字符与数字数据混合在一起的可能性较小（尽管仍然可能）。但是，具有数字特征的数据集可能具有不正确的值或缺失值（稍后讨论）。此外，计算特征值的最小值、最大值、平均值、中值和标准差显然只与数值有关。
预处理步骤完成后，进行数据整理，即将数据转换为新的格式。您可能必须将来自多个来源的数据合并到一个数据集中。例如，您可能

## 机器学习代写|自然语言处理代写NLP代考|DATA TYPES

• 数字数据（整数/浮点和离散/连续）
• 字符/分类数据（不同语言）
• 日期相关数据（不同格式）
• 货币数据（不同格式）
• 二进制数据（是/否、0/1 等）
• 标称数据（多个不相关的值）
• 序数数据（多个相关值）
考虑一个包含房地产数据的数据集，该数据集可以有多达 30 列（甚至更多），通常具有以下特征：
• 房屋中的卧室数量：数值和离散值
• 平方英尺数：一个数值和（可能）一个连续值
• 城市名称：人物资料
• 施工日期：日期值
• 售价：货币价值，可能是连续价值
• “待售”状态：二元数据（“是”或“否”）
名义数据的一个例子是一年中的季节：尽管许多国家有四个不同的季节，但有些国家只有两个不同的季节。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。