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Articles by Devashree Madhugiri
Data-Driven Decision Making with Sentiment Analysis in R
Leveraging the Quanteda, Textstem and Sentimentr Packages to Extract Customer Insights and Enhance Business Strategy In a rapidly evolving world that is getting more and more AI-driven every instant, businesses now need to constantly seek a competitive edge to remain sustainable. Companies may do this by regularly observing and analyzing customer opinions regarding their products and services. They achieve this by assessing comments from many sources, both online and offline.
Data-Driven Decision Making with Sentiment Analysis in R
In a rapidly evolving world that is getting more and more AI-driven every instant, businesses now need to constantly seek a competitive edge to remain sustainable. Companies may do this by regularly observing and analyzing customer opinions regarding their products and services. They achieve this by assessing comments from many sources, both online and offline.
How to Perform Hyperparameter Tuning in R with Python
Optimize your machine learning models with Reticulate & Optuna Data Science and AI professionals usually spend a significant amount of time gathering data, cleaning it, preparing it, and choosing the perfect algorithm while building a machine learning model to generate predictions. However, the model performance does not always seem to meet the desired expectations. This happens because an important step has not been covered after setting up the baseline model.
5 Python Packages For Geospatial Data Analysis
Geospatial data analysis is critical in urban planning, environmental research, agriculture, and transportation industries. The growing need has led to an increase in the use of Python packages for various geographic data analysis requirements, such as analyzing climate patterns, investigating urban development, or tracking the spread of diseases, among others.
Devashree Madhugiri
Devashree Madhugiri Devashree Madhugiri holds an M.Eng degree in Information Technology from Germany and has a background in Data Science. She likes working on different Machine Learning and Deep Learning projects. She enjoys sharing her knowledge in AI by writing technical articles related to data visualization, machine learning, computer vision on various technological platforms. She is currently a Kaggle Notebooks Master and loves solving Sudoku puzzles in her leisure time.
Top 3 Ways to Import Data into R using Copy and Paste
Introduction R is a powerful and versatile programming language used for statistical analysis, data visualization, and machine learning. The success of a data analysis project relies on properly importing the data into R. While there are several methods to import data into R, one of the simplest and most convenient methods is to copy and paste data from external sources. These sources are spreadsheets, text editors, or websites.
Creating Interactive and Animated Charts with ipyvizzu
Advanced Business Analytics Data Analysis Data Science Data Visualization Python Introduction Data visualization (DV) plays a crucial role in analyzing and interpreting data. It helps to identify patterns, trends, and relationships in large and complex datasets, making it easier to communicate insights and findings to a broader audience. With the growing importance of data science and machine learning, data analysis holds a special place in many industries and fields.
Important Keras Questions for Cracking Analytics Interviews
Computer Vision Data Science Deep Learning Intermediate Interview Questions Libraries Introduction Nowadays, the corporate environment changes according to technology. Organizations are converting them to cloud-based technologies for the convenience of data collecting, reporting, and analysis. This is where data warehousing is a critical component of any business, allowing companies to store and manage vast amounts of data.
11 Popular R Packages for Beginners in 2023
Beginner Libraries Listicle R This article was published as a part of the Data Science Blogathon. Introduction These packages provide a range of functions, including data manipulation and wrangling, data visualization, machine learning, dynamic documentation, as well as the date and time handling. These packages are essential tools for working with data in R. This article presents these 11 libraries categorized as per the specific tasks performed using them.
Three R Libraries for Automated EDA
This article was published as a part of the Data Science Blogathon. Introduction With the increasing use of technology, data accumulation is faster than ever due to connected smart devices. These devices continuously collect and transmit data that can be processed, transformed, and stored for later use. This collected data, known as big data, holds valuable insights which can be harnessed across different industries to solve real-world problems.
Top 7 Packages for Making Beautiful Tables in R
Learn how to build attractive data tables with R packages Developing meaningful visualizations is an essential yet challenging task in data science. Along with the correct choice of tools, a general understanding of the target audience and the goal of the analysis is also expected in any data analysis. In the same context, tables are a powerful and efficient way of organizing and summarizing any available dataset, especially containing categorical variables.
Top 7 Packages for Making Beautiful Tables in R
Developing meaningful visualizations is an essential yet challenging task in data science. Along with the correct choice of tools, a general understanding of the target audience and the goal of the analysis is also expected in any data analysis. In the same context, tables are a powerful and efficient way of organizing and summarizing any available dataset, especially containing categorical variables.
Extract Text from Images Quickly Using Keras-OCR Pipeline
This article was published as a part of the Data Science Blogathon. Introduction Although plenty of digital information is available for consumption by businesses, employees still have to handle printed invoices, flyers, brochures, and forms in hard copies or textual images saved in .jpg,.png, or .pdf formats. Handling such data manually in these files is tedious, time-consuming, and prone to manual errors.
rbokeh: How To Create Interactive Plots In R
Data visualization is an essential aspect of AI. Data visualizations let you derive insights from data and enable you to communicate about the data with others. There are many software packages that we can use to visualize data and build meaningful dashboards. While making visualizations in Python, we have different libraries like Matplotlib, Seaborn, Altair, etc., and ggplot2, lattice when using R. Interactivity in data visualizations takes them to the next level.
Interactive Data Visualization using rbokeh
This article was published as a part of the Data Science Blogathon. Introduction Data VisualizationData Visualization is used to present the insights in a given dataset. With meaningful and eye-catching charts, it becomes easier to communicate data analysis findings. Several charts are available for specific purposes like bar charts to present categorical distribution, line charts to present time series information, etc.
Building a Deep Learning Image Classifier with Keras using R
Beginner Computer Vision Deep Learning Image Analysis Python This article was published as a part of the Data Science Blogathon. Introduction An important application of deep learning and artificial intelligence is image classification. Image classification is the process of labeling images based on specific characteristics or features that they contain. The algorithm recognizes these qualities and utilizes them to distinguish between images and assign labels to them.
Build a Step-by-step Machine Learning Model Using R
Intermediate Libraries Machine Learning Python This article was published as a part of the Data Science Blogathon. Introduction to Machine Learning Model Machine learning is changing the approach of businesses in the world. Every company, large or small, aspires to find insight from the large amounts of data it stores and processes regularly. The desire to predict the future motivates the work of business analysts and data scientists in domains ranging from marketing to healthcare.
Apache Spark Vs. Hadoop MapReduce – Top 7 Differences - Analytics Vidhya
This article was published as a part of the Data Science Blogathon. Introduction Apache Spark was released in 2014. Earlier to it, Hadoop MapReduce was the main focus for processing large data with no competitors. While Spark is the new and widely used technology, MapReduce still has its place in specific applications. Let’s take a deeper look at two popular tools for big data, MapReduce and Spark, and compare them to decide which is best in specific circumstances. What is Apache Spark?
Deploying a Keras Flower Classification Model
This article was published as a part of the Data Science Blogathon. Background on Flower Classification Model Iion to Flower Classification In the same context, flower images are always pleasant to look at. We find a variety of attractive flowers in nature with different shapes and enchanting colors. At times, we see a flower we like but cannot identify. Computer vision can help us correctly identify the flower species in such a situation.
Building a Food Image Classifier using Fastai
This article was published as a part of the Data Science Blogathon. Background Social Media platforms are a common way to share interesting and informative images. Food images, especially related to different cuisines and cultures, are a topic that appears to be frequently trending. Social media platforms like Instagram have a large number of images belonging to different categories.
Getting Started with NoSQL Database Called HBase
Database Intermediate SQL This article was published as a part of the Data Science Blogathon. Introductio HBase is a database for storing and retrieving data with random access. In other words, we can write data as per our requirements and read it back again as needed. HBase works well with both structured and semi-structured data. This means storing structured data like relational tables and semi-structured data like tweets or log files together is possible.
Classification and Regression using AutoKeras
This article was published as a part of the Data Science Blogathon. Introduction on AutoKeras Automated Machine Learning (AutoML) is a computerised way of determining the best combination of data preparation, model, and hyperparameters for a predictive modelling task. The AutoML model aims to automate all actions which require more time, such as algorithm selection, code writing, pipeline creation, etc.
Get to Know Apache Flume from Scratch!
Beginner Data Engineering Hadoop This article was published as a part of the Data Science Blogathon. Apache Flume, a part of the Hadoop ecosystem, was developed by Cloudera. Initially, it was designed to handle log data solely, but later, it was developed to process event data. The Apache Flume tool is designed mainly for ingesting a high volume of event-based data, especially unstructured data, into Hadoop.
5 Python Libraries for Time-Series Analysis
Intermediate Libraries Python This article was published as a part of the Data Science Blogathon. Introduction on Time-Series A Time-Series is a sequence of data points colled changes over time. The time-series models can be univariate or multivariate. The univariate time series models are implemented when the dependent variable is a single time series, like room temperature measurement from a single sensor.
A Comprehensive Guide to Sharding in Data Engineering for Beginners
This article was published as a part of the Data Science Blogathon. Big Data is a very commonly heard term these days. A reasonably large volume of data that cannot be handled on a small capacity configuration of servers can be called ‘Big Data’ in that particular context. In today’s competitive world, every business organization relies on decision-making based on the outcome of the analyzed data they have on hand.
Let's Understand Data Augmentation Using AugLy!
This article was published as a part of the Data Science Blogathon. Image Source: Author Introduction A common problem with machine learning and deep learning models is ‘Overfitting.’ This means that the model accuracy is very high on the training dataset but fails to do so on the unknown data. To address the challenge of overfitting, we can increase the size of the dataset, i.e., exposing the model to new data for a better generalization.
Visualize Deep Learning Models using Visualkeras
This article was published as a part of the Data Science Blogathon. Image Source: Author Introduction Deep learning, a subset of machine learning, is undoubtedly gaining popularity due to big data. Startups and commercial organizations alike are competing to use their valuable data for business growth and customer satisfaction with the help of deep learning models.
A Comprehensive Guide on ggplot2 in R
This article was published as a part of the Data Science Blogathon. Image source: Author Introduction Visualization plays an important role in the decision-making process after analyzing relevant data. Graphical representation highlighting the interdependence of key elements affecting performance is important in the above process.
This article was published as a part of the Data Science Blogathon. Image Source: Author Jupyter Notebook is an easy-to-use, open-source tool for web-based interactive computing. The Jupyter Notebook supports more than 40 different programming languages like R, Python, Java, etc. Therefore, most data science professionals tend to use Jupyter Notebooks to create and share documents, including code, equations, visualizations, computational outputs, markdown text, etc.
Web scraping with PyScrappy
This article was published as a part of the Data Science Blogathon. Image source: Author Introduction These days Data Science professionals are expected to have some background and experience in ‘Web Scraping.’ This technique involves gathering relevant data from one or more web pages or websites using a tool like Selenium. To build a web scraper, one needs expertise in Selenium and Python coding, commonly used for this purpose.
Auto-Sklearn: Accelerate your machine learning models with AutoML
This article was published as a part of the Data Science Blogathon. Introduction AutoML is a relatively new and upcoming subset of machine learning. The main approach in AutoML is to limit the involvement of data scientists and let the tool handle all time-consuming processes in machine learning like data preprocessing, best algorithm selection, hyperparameter tuning, etc., thus saving time for setting up these ML models and speeding up their deployment.
Introduction to Cloud Computing for Machine Learning Beginners
Image Source: Author Cloud computing is an important term for all Data Science and Machine Learning Enthusiasts. It is unlikely that you may not have come across it, even as a beginner. But why is it important, one might ask. A simple explanation for this is that as the dataset expands, i.e., more samples and features are added, the machine learning model becomes more complex.
How To Use Python To Analyse Fitness Tracker Market: Step By Step EDA
This article was published as a part of the Data Science Blogathon Image Source: Author Introduction to Fitness Tracker Market With the advancements in the IT domain, wearable devices have been in great demand in the recent past. A wearable device is simply a device that can be worn by the user and this device is capable of collecting vital information about the activities and health of the user through smart sensors. Fitness trackers are an important type of wearable device.
Beginner’s Guide to AutoML with an Easy AutoGluon Example
Machine Learning is popular and is being used everywhere for applications ranging from financial services to healthcare, marketing & advertising to manufacturing. Almost all industries seem to derive substantial benefit using some form of Machine Learning. Over the recent past, automation technology also seems to be picking up along with Machine Learning. When these two are combined, we arrive at something amazing called ‘automated machine learning’ or simply ‘AutoML’.
How To Build A Treemap In 3 Ways Using Python
Introduction Quite often we have a requirement to visualize categorical data in a dataset. For visualizing such a type of data, there are several different options to choose from like the pie charts, horizontal bar charts (that indicate percentages of the categories), waffle charts, and Venn diagrams, etc. These charts help in displaying the proportions of part-to-whole to the reader.
Understanding Machine Learning Models Better with Explainable AI
Image Source: Author (Designed in Canva) It is interesting to decipher the working of Machine Learning through a web-based dashboard. Imagine gaining access to the interactive plots displaying information on model performance, feature importance as well as What-if analysis. What is exciting is that one does not need any web development expertise to build such an informative dashboard but simple few lines of python code are sufficient to generate a stunningly interactive Machine Learning Dashboard.
Building an Interactive Dashboard using Bokeh and Pandas
This article was published as a part of the Data Science Blogathon The Importance of Data Visualization A huge amount of data is being generated every instant due to business activities in globalization. Companies are extracting useful information from such generated data to make important business decisions.
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