DEV Community: Rono

Getting Started with Sentiment Analysis

Rono — Sun, 26 Mar 2023 18:30:21 +0000

INTRODUCTION

Machine learning is a powerful technology that simplifies data-related systems. This technology is not just limited to numeric data, it can be employed in text-based datasets that may even be subjective. Natural Language Processing (NLP) is a branch of artificial intelligence that involves the development of algorithms and techniques that enables computers to understand, interpret and generate human language. Sentiment analysis is one of the applications of this branch.

WHAT IS SENTIMENT ANALYSIS

Sentiment according to Oxford learners dictionaries is a feeling or an opinion, especially one based on emotions. Likewise, sentimental analysis deals with analyzing an opinion or view that is expressed by someone. Formally, sentimental analysis is a technique that is used to analyze text data, to determine the overall sentiment expressed in it.

The goal of the sentimental analysis is to distinguish whether the feeling or opinion expressed in the text is positive, negative or neutral. Trivial as it may sound, this analysis is important for several reasons including:

Marketing, where companies use it to identify topics and language that resonate with their prospective customers thus informing their text/print marketing campaigns.
Reputation management: companies leverage sentiment analysis to monitor their online reputation and address negative sentiments promptly.
Rating customer experience: by analyzing customer feedback and sentiment, organizations get to understand how clients perceive their products, services or brand. They can then identify ways to provide better customer experiences.
Product development: as a developer, you would want to know how people perceive your product. You can use sentiment analysis to get insights into customer preferences and pain points, which you in turn use to inform your product development and innovation.
Competitive analysis: you can use sentiment analysis to monitor your competitors by getting how customers perceive them. You can then identify opportunities for differentiation and competitive advantages.

Enough of what and why sentiment analysis, we’ll now answer the how. The next part outlines simple and basic steps to carry out sentiment analysis. Sentiment analysis algorithms typically use machine learning and statistical techniques to automatically categorize people’s views as positive, negative or neutral based on the choice of words they use, context and other factors. We will use a sample dataset containing tweets and classify them as either positive or negative.

Define the problem
Collect and preprocess
Label data
Choose a model
Train and evaluate your model
Deploy your model

Define the problem

{

STEPS TO DO SENTIMENT ANALYSIS
TOOLS NEEDED
PROCEDURE
SUMMARY/CONCLUSION
}

Basic SQL Commands for Data Science

Rono — Sat, 18 Mar 2023 04:55:57 +0000

Exploratory Data Analysis Guide

Rono — Fri, 03 Mar 2023 14:47:15 +0000

What is exploratory data analysis

Data is the current invaluable resource that when utilized appropriately provides helpful information that shapes the future of the world. Data in its raw nature is rather a useless chunk, one cannot use it to make informed decisions. Therefore, one needs to transform it to extract insights from the data. To perform this action, one should first understand the data, and this is where exploratory data analysis comes in. Data analysis is a crucial component in various fields from data science to business. The first and crucial step in this process is exploratory data analysis.

Exploratory data analysis put simply is, getting to know your data to understand it. To do this, examine the dataset keenly to identify patterns and anomalies. Manipulate and visualize the data to generate insights that inform the further analysis.

Objectives of exploratory data analysis

There are two main goals of exploratory data analysis:

Gain a deep understanding of the data, its quality and structure.
Get a wider perspective of the data, understand the relationship between variables, and use relationships in features to develop models for the data.

Types of exploratory data analysis

There are two major types of exploratory data analysis:
• Univariate exploratory data analysis
• Multivariate exploratory data analysis

Univariate EDA

Here, uni stands for unity, which is just a single value, while variate comes from the variable, meaning in univariate EDA there is only one variable that we examine in isolation. Techniques employed in univariate EDA include performing summary statistics on the measures of central tendencies and dispersion. Use histograms and boxplots to visualize the data graphically. A histogram is a graph in which each bar along the x-axis represents the frequency distribution of the data. A box plot is a rectangle drawn to represent the 5-number statistics of data. These are minimum value, maximum value, median, lower quartile and upper quartile.

Multivariate EDA

Here, multi stands for multiple and variate stands for variable, meaning multivariate EDA is where you consider multiple variables in a dataset. The goal is to determine the correlations between the variables. Multivariate EDA techniques include:

Scatter plots; this a graphical plot of two quantitative variables on the x-y axis.
Heatmaps; are graphical representations where data values are represented as colors.
Correlation matrix; displays pairwise correlation coefficients between all pairs of variables in a dataset.

Exploratory data analysis tools

The choice of data analysis tool to use largely depends on what data you are working on, what tool your organization use and which tool you are comfortable with. For instance, you can use Python for general datasets and MATLAB for datasets in the engineering field. In this article, we shall use Python as our tool for exploratory data analysis.

Exploratory data analysis steps

There are four main steps involved in exploratory data analysis. These are data collection, cleaning, analysis and visualization. The steps are performed in chronological order.

Data collection

This is the process of gathering data from various sources and consolidating them into one datasheet. Sites like Kaggle UCL Machine Learning Repository, EarthData, AWS Open Datasets, and Github among others provide public datasets for developers and data scientists.

In the following illustrations, we use the “IT Salary Survey for Euregion (2018-2020) dataset” from Kaggle.
First, import all the necessary libraries you’ll need.

Next load the datasets into data frames:

Here there are three dataframes but we would wish to combine them into one dataframe. To combine them into one file we use the concatenate function. After combining we write the combined dataframe into a CSV file.

Display the first 5 rows of the dataframe:

Get the summary of the dataframe using the info () function this help in checking the structure and completeness of the dataframe. It also tells us the datatypes in the dataframe.

Get the descriptive information of the dataframe. Use the .describe() function to perform this operation:

Data cleaning

Datasets may have inconsistencies or undesired values; therefore you may need to clean your data.
Check if there are any missing values in the data frame by using the function .isna()

Our data has numerous features. We would however wish to work on just a few specific features. So, we will use the “loc” method of the pandas dataframe to create a new dataframe with selected specific features.

Check for missing values using the isna() function

If your data has missing values, you should consider checking the percentage of missing values. The pandas function df.isna().mean()*100 returns the percentage of missing values in each column. Once you get information about the missing values, there are approaches you can take to handle them.

Handling missing values

Missing values can either make or break your subsequent data analysis. Therefore you must handle them appropriately. The following are some commonly used methods:

Dropping missing values; is simply getting rid of the rows or columns containing the missing values. It is more appropriate when the missing data is small compared to the entire dataset. Thus removing them will be insignificant to the analysis. Use the function drop.na to remove missing values.
Fill in missing values; use the function fiil.na() to fill in the missing values with either mean or median. Care should be taken to avoid generating misleading values.
Interpolating missing values; this method returns a dataframe with missing values replaced with values obtained from interpolating neighbouring rows or columns. Use the function interpolate()

In the salary 2018 dataset, we replaced the missing values with the median.

Data analysis

This is where you explore the data to determine and identify correlations that exist. This could either be a univariate or bivariate analysis depending on the dataset you are working on. Below is the pi-chart plot of gender distribution.

Use the function corr() to check if there are any correlations in the dataset. Below is the correlation matrix of the salary2018 dataset.

To visualize the correlation, use the seaborn library to generate a heatmap as shown below:

From the heatmap, it is observable that the current salary has a strong correlation with the salary one & two years ago. Age has a minimal correlation with salaries in all the years but has a stronger correlation with years of experience.

In conclusion, EDA is an important step in the data analysis process that enables data scientists to gain accurate insights and trends from their data. It is important, to note that EDA is an iterative process. Steps taken may vary depending on the dataset and the objectives of the analysis.

Introduction To Python for Data Science

Rono — Sat, 18 Feb 2023 20:50:56 +0000

Python is continuously becoming one of the most widely used programming languages in the Data Science and Artificial intelligence field. This is majorly because of its simple syntax, flexibility and plenty of powerful open-source libraries and frameworks that make data analysis and visualization as juicy as your favorite cocktail. This article is best suited for a beginner who is looking for a way to dive into data science. It introduces you to the basics of Python for data science and some of the key libraries and tools used in the field. For an experienced data scientist wanting to reminisce about their journey into data science this is for you!

The Basics

Python is a high-level, open-source and interpreted language, this is to say that the code is executed line-by-line rather than compiled into machine code. Open-source software is computer software that is made available under a license that allows its users to access, modify and distribute its source code freely. Open-source software is often developed collaboratively by a community of developers who contribute to improving the software. An interpreted programming language is one in which lines of code are executed directly by an interpreter without the need to compile. It allows programmers to quickly write and test code in the shortest time possible.

Python code is written and runs using the command-line interpreter, integrated development environment (like vs code) and notebooks (like jupyter). The syntax is straightforward to read even for a complete beginner, it's just natural. Unlike other languages that use braces or brackets to denote code blocks, Python uses indentation which makes it easy to follow and debug.

example of python code

Python has in-built data types that include:

Numeric type: these include integer, floating-point and complex numbers
Sequence type: these include lists, tuples and ranges
Boolean type: represents truth values output is either true or false
Text type: this is a string data type used to represent a sequence of characters
Set type: includes sets and frozen sets
Mapping type: this is the dictionary data type it stores key-value pairs

In addition to the above in-built data types, Python allows users to create their own custom data types using classes and objects. The multiple data structures can be used to store and manipulate large amounts of data, making Python an outstanding tool for data analysis.

Data Science

Data science is an interdisciplinary field that uses statistical and computational methods to extract insights and knowledge from data. It involves applying techniques and algorithms to large datasets to discover patterns, extract meaningful information and thus make data-driven decisions.
The process of data science is summarized in the following stages:

Data collection: this is the act of gathering data from data sources including databases, APIs and web scraping.
Data cleaning; one you have your data, you need to clean it and transform the data to remove errors, inconsistencies and missing values.
Data analysis; once you have data ready, you now carry out
analysis using statistical methods and machine learning
algorithms to discover patterns and insights.
Data visualization: this is the part where you present
your analysis in a graphics method to communicate the
insights effectively
Data interpretation: this is where you put your
visualized data into use by extracting meaningful insights
and knowledge.
Model deployment: here you deploy the model developed from
your data to make predictions and decisions.

Data science techniques

The following are some of the most commonly used techniques in data science:

Machine learning: this is a subfield of artificial intelligence that involve using algorithms to learn patterns from data. Thereafter, you build predictive models, classification models and clustering models.
Data mining: this is the process of discovering patterns from the data to extract insights and knowledge from large datasets
Natural language processing: this is the process of analyzing and understanding human language. It is mainly used on unstructured data such as text
Data visualization: this is where data is graphically represented

Data science applications

Data science is utilized in various domains. The most common ones are:

Business analytics: in this domain data science is used to extract insights and knowledge from data to make informed business decisions.
Health care: data science is used in healthcare to analyze patient data, diagnose diseases and develop treatment plans
Finance: here financial data is used to detect fraud and make investment decisions.
Marketing: data science is used in marketing to analyze customer data, identify customer segments and develop targeted marketing campaigns.

Data science is a powerful evolving field that has and continues to revolutionize the way we make decisions and solve problems from an informed point. As more and more data becomes available, the field continues to grow in importance and impactful on us.

Python Libraries for Data Science

A library is a collection of pre-written code that developers can use to perform specific tasks. It provides functions and classes that developers can use directly in their code to accomplish a specific task. Developers control how they use the library and therefore, can choose which functions and classes they want to use.

Python has an extensive ecosystem of open-source libraries, and frameworks and a large base of the community. You can think of libraries as pre-written code for common tasks in data analysis, such as cleaning and processing data, statistical analysis and machine learning. Here are the most commonly used libraries in data science.

NumPy

NumPy is a library for numerical computing. It provides fast and efficient arrays, matrices and mathematical functions to work on large datasets. It provides support for large multi-dimensional arrays and matrices as well as mathematical functions for manipulating the arrays. Numpy is the core library for scientific computing in the Python language and for this reason, it is extensively used in data science, machine earning and the areas of scientific computing.

NumPy’s main object is the N-dimensional array, which is a homogeneous collection of values that can be indexed and sliced like the usual Python list. Unlike lists, however, NumPy arrays can be multi-dimensional. Numpy supports a range of mathematical operations such as addition, subtraction, multiplication and trigonometric functions which can be performed element-wise on entire arrays. Numpy also includes a range of functions for linear algebra, Fourier analysis and nearly all calculus functions and operations. This library can be used together with other Python libraries for data analysis, such as Pandas and Matplotlib.
To install Pandas on your IDE, just run the code “pip install Numpy” on the terminal.

Pandas

When you want to manipulate and analyze data, Pandas is your go-to library. It provides a simple and efficient way to work with structured data such as data frames and series that includes; cleaning, filtering, grouping and transforming.

The primary data structure n Pandas is the Data frame, which is two-dimensional. Data frames can be created from CSV files, excel spreadsheets and SQL databases. Once created, data frames can be manipulated using a range of functions such as selecting and filtering rows and columns, grouping and aggregating data, and merging and joining tables. You can perform operations such as removing duplicates, filling in missing data and converting data types. It also includes tools for working with time series data, such as resampling, shifting and rolling window operations.
Apart from the data frames, pandas also provide the series data structure, which is a one-dimensional array with labelled indices.

Matplotlib

Presenting processed data in a visual design is a good practice for easy interpretation. This otherwise tedious task is simplified by the matplotlib library. It provides a range of visualizations for data analysis, including line and pie charts, scatter plots, bar graphs and histograms. Matplotlib is a python library for creating static, animated and interactive visualizations in python.

Matplotlib’s main interface is the Pyplot module, which provides a simple way to create and customize plots. It also provides support for working with multi-panel figures, creating custom color maps, and adding annotations to plots. Matplotlib can be used with a variety of backends to generate static images and interactive backends. Matplotlib is widely used in data science and scientific computing for creating visualizations of data. It can be used together with other libraries for data analysis, such as NumPy, pandas and seaborn.

Seaborn

This is a library based on matplotlib that provides a high-level interface for creating informative and attractive statistical graphics. It is built on top of matplotlib and provides a range of built-in themes and color palettes that make it easy to create visually appealing plots with minimal customization. This library supports heatmaps, categorical plots and time series plots.

In addition to quantitative data visualizations, seaborn provides support for working with categorical data, such as grouping data by a specific feature or creating visualizations that compare multiple groups. Seaborn is often used in data science and machine learning for exploratory data analysis and communicating insights from data. It can be used in conjunction with the other libraries.

Getting Started with Python for Data Science

To get started with python for data science, you’ll need to download and install Python from the organization platform here: Download Python | Python.org and the key libraries highlighted above. Python has several Integrated Development Environments (IDEs) that are popular among data scientists. An integrated development environment is a software application that provides comprehensive facilities for software development. Typically, it includes a code editor, debugger, and build automation tools, along with other features like version control, code profiling as well as project management tools. IDEs are purposefully designed to streamline and provide an efficient workflow for developers. They often provide intelligent code completion and syntax highlighting to make it easier for you to code quickly and accurately. There are many IDEs you can choose from, each of which is tailored to specific programming languages or platforms. This way, you get to choose the most appropriate IDE for you. Below are some of the good Python IDEs:

PyCharm

This IDE is developed by JetBrains and is available as a commercial and free, open-source version called PyCharm Community edition. You can download it here: PyCharm: the Python IDE for Professional Developers by JetBrains. Some of the notable features of PyCharm include:

Code highlighting and intelligent code completion
Built-in debugger
Integration with version control systems
Database tools for working with SQL and NoSQL databases
Refactoring tools to improve code quality and maintainability
Support for multiple python versions and virtual environments

PyCharm is a popular choice among python developers due to its feature-rich environment, ease of use and customizability. Notably, PyCharm also supports web development frameworks like Django, Flask, Pyramid and web2py.

Spyder

This is an open-source IDE for scientific Python development. It is designed for data science, numerical computation and scientific computing. Therefore, Spyder is well-suited for scientists, engineers and data analysts. Notable features of Spyder include:

Interactive console for live coding
Variable explorer for inspecting and manipulating data
Integrated debugger with breakpoints and variable inspection
Support for scientific libraries
Code analysis and linting tools to improve code quality
Git version control integration Spyder is popular among scientists and data analysts for its ease of use and simplicity. it also provides extensive documentation and support for users to get started with scientific Python Development. You download it from the official Spyder IDE organization page.

Jupyter Notebook

Jupyter Notebook is a web-based interactive computing environment that allows you to create and share documents containing executable code, equations, visualizations and narrative texts called markdown. You can access the web-based interface on your web browser. Code cells run in the notebook, and produce output visualizations that are displayed inline. The following are key features of the Jupyter Notebook:

Interactive code execution
Integrated visualization and graphing capabilities
Support for data exploration and analysis
Collaboration and sharing features
Rich text formatting with markdown syntax

Although not a fully-fledged IDE, the Jupyter notebook is widely used by data scientists, researchers and developers for data analysis, prototyping and experimentation. It is a popular tool in science due to its flexibility and ability to combine code and narrative text in a single document. Click on the following link: Jupyter to install the notebook. For additional functionality, you can extend through plugins and extensions.

Visual Studio Code

Visual Studio Code abbreviated VS Code is a popular cross-platform, free and open-source code editor developed by Microsoft. It boasts its support of a wide range of programming languages and frameworks, python included. Key VS Code features include:

Customizable user interface
Live collaboration with other developers
Integrated debugging with breakpoints and variable inspection
Intelligent code completion and syntax highlighting
Large extensions marketplace with a variety of tools and plugins
Git integration for version control
Built-in terminal for command-line access.

VS Code’s lightweight, fast performance, and wide range of additional tools and plugins make it a popular choice among developers. You can download it here: VS Code

Anaconda

Anaconda is a popular Python distribution that comes with over 1000 useful scientific computing, data analysis and machine learning packages. It provides a command-line interface (CLI) called Anaconda Prompt which allows users to create and manage environments, install packages and run python scripts. Anaconda includes the Spyder IDE and the Jupyter Notebook mentioned earlier. The graphical user interface called Anaconda Navigator allows users to manage environments, install packages and launch applications in just a few clicks.

While Anaconda does not include a full-fledged IDE, it is a popular choice for data scientists, researchers and developers who need a comprehensive Python environment. You can download it from here: https://www.anaconda.com/

After installing your preferred IDE, you install the necessary libraries. A shortcut to this is installing the Anaconda distribution, which includes Python and most of the tools commonly used in data science. Once you have installed them, you can start working with data in Python. In the next issue, we’ll talk about steps in data analysis from reading data from a file to visualization.

Python is an excellent programming language for data science. It provides a wide range of libraries and tools for data analysis and data visualization. It is also easy to learn and has a large community of developers and users who share their knowledge and experience. If you are interested in data science, Python is an excellent language for you to learn.