Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,

K-Means Clustering Concepts & Python Example

Clustering is a popular unsupervised machine learning technique used in data analysis to group similar data points together. The K-Means clustering algorithm is one of the most commonly used clustering algorithms due to its simplicity, efficiency, and effectiveness on a wide range of datasets.  In K-Means clustering, the goal is to divide a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean value. The algorithm works by iteratively updating the cluster centroids until convergence is achieved. In this post, you will learn about K-Means clustering concepts with the help of fitting a K-Means model using Python Sklearn KMeans clustering implementation. You will …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Mann-Whitney U Test (Wilcoxon Rank Sum): Python Example

wilcoxon rank sum hypothesis explanation

In the ever-evolving world of data science, extracting meaningful insights from diverse data sets is a fundamental task. However, a significant problem arises when these data sets do not conform to the assumptions of normality and equal variances, rendering popular parametric tests like the t-test ineffectual. Real-world data often tends to be skewed, includes outliers, or originates from an unknown distribution. For instance, data related to salaries, house prices, or user behavior metrics often challenge traditional statistical methods. This is where the Wilcoxon Rank Sum Test, also known as the Mann-Whitney U test, proves to be an invaluable statistical test. As a non-parametric alternative to the independent two-sample t-test, it …

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Posted in Data Science, statistics. Tagged with , .

ChatGPT Prompts Design Tips & Examples

Are you looking to unlock the full potential of ChatGPT? Do you want to learn how to design & create engaging and effective prompts that can help you generate high-quality responses? Look no further! In this blog, we’ll share some expert tips and examples on how to design ChatGPT prompts that get the most out of this powerful language model. As one of the most advanced large language models available today, ChatGPT has the ability to generate informative and engaging responses. But the key is to provide clear instructions and ask right questions if we want to get the best results. That’s where prompt design & engineering comes in. By …

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Posted in ChatGPT, Generative AI. Tagged with , .

Dashboard Design Best Practices: Examples

dashboard design best practices

Are you looking to create effective, user-centric, and highly actionable data dashboards? Do you want your dashboard to not just present data, but tell a story that compels your team to make informed decisions? In an age of data-driven decision making, dashboards have become an indispensable tool for product managers, data analysts, and data visualization experts alike. A well-designed dashboard provides a real-time visual snapshot of performance, highlights crucial metrics, and assists in spotting trends or anomalies. However, designing a good dashboard is both an art and a science. It demands a deep understanding of users’ needs, a strategic approach to information organization, and an adept use of data visualization …

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Posted in Data analytics, Data Science, Data Visualization. Tagged with .

Data Science & Big Data Career Paths

data science big data career paths

Navigating the world of data science can be as complex as the data sets that these professionals work with. As the field continues to evolve at a rapid pace, the array of job roles and career paths have expanded, encompassing a multitude of specializations ranging from Data Analysts and Machine Learning Engineers to Data Scientists. This dynamic landscape offers a wealth of opportunities, but it can also create confusion for those looking to embark on or advance their careers in data science. In this blog, we aim to demystify these career paths in data science, offering clarity on the progression of roles, responsibilities, and skills needed for each. Whether you …

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Posted in Big Data, Career Planning, Data Science, jobs, Machine Learning.

Types of Data Visualization: Charts, Plots Examples

In today’s data-driven world, the ability to extract insights from vast amounts of information has become a critical skill for data scientists and analysts. Visualizing data through charts, graphs, and other types of visual representations can help them uncover patterns and relationships that might be difficult to spot otherwise. However, not all visualizations are created equal, and choosing the right type of visualization can make all the difference in communicating insights effectively. That’s why understanding the different types of visualization available is crucial for data visualization experts and data scientists. In this blog, we’ll explore some of the most common types of visualization, including comparison plots, relation plots, composition plots …

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Posted in Data Science, statistics. Tagged with , .

Dynamic Pricing & Machine Learning: Strategies, Examples

dynamic pricing machine learning - strategies examples

Are you a product manager looking to maximize profits and improve product performance, or a data scientist eager to leverage the power of machine learning to solve complex business problems related to dynamic pricing? Do you ever wonder how businesses can optimize their pricing strategies to stay competitive, cater to customer expectations, and enhance their market positioning? In this blog, we uncover the intersection of advanced AI technologies with smart pricing strategies. In an era where customer expectations, market trends, and competitor actions change rapidly, businesses need an agile and data-driven approach to pricing. That’s where dynamic pricing coupled with machine learning comes into play. We’ll explore compelling strategies, reveal …

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Posted in Finance, Machine Learning. Tagged with .

Customer Segmentation in Banking: Examples

Customer Segmentation Machine Learning Use Cases in Banking

Ever wondered how some banks seem to know exactly what their customers need, almost before the customers do? They’re probably leveraging the power of customer segmentation. We all know how vital it is for any businesses including banks to truly understand their customers in today’s competitive landscape. And that’s where the magic of customer segmentation comes into play. It is enabling banks to dive deep into customer data and extract actionable insights, influencing everything from crafting personalized experiences to strategic decision making. In this blog post, we’re going to learn about customer segmentation use cases in banking, showcasing how it’s going to impact product development, risk management, and customer acquisition. …

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Posted in Banking, Finance. Tagged with .

Machine Learning Use Cases in Finance: Concepts & Examples

machine learning finance use cases

What if we can build solutions that could predict financial market trends, assess credit risk with unerring precision, detect fraudulent activities before they occur, and significantly automate your day-to-day operations? Such solutions can be called as predictive analytics solutions which leverages AI / machine learning for making predictions. Machine learning has found its way into finance and is being used in various ways to improve the industry. Finance has always been a data-driven industry, and in recent years, machine learning has become an increasingly important tool for making sense of that data. In this blog post, we will explore some of these use cases and explain how machine learning is …

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Posted in Data Science, Finance, Machine Learning. Tagged with , , .

Kruskal Wallis H Test Formula, Python Example

Kruskal Wallis H Test: Formula, Python Example

Ever wondered how to find out if different groups of people have different preferences? Maybe you’re a marketer trying to understand if different age groups prefer different features in a smartphone. Or perhaps you’re a public policy researcher, trying to determine if different neighborhoods are equally satisfied with their local services. How do you go about answering these questions, especially when the data doesn’t follow the typical bell-shaped curve or normal distribution? The solution lies in the Kruskal-Wallis H Test! This is a non-parametric test that helps to compare more than two independent groups and it comes in really handy when the data is not bell-shaped curve data or not …

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Posted in Data Science, Python, statistics. Tagged with , , .

Weighted Regression Model Python Examples

Weighted regression model python example

Have you ever wondered how regression models can be enhanced to provide more accurate predictions, even in the presence of outliers or data points with varying significance? Enter weighted regression machine learning models, an approach that assigns weights to data points, allowing for precise adjustments and improvements in prediction accuracy. In this blog post, we will learn about the concepts of weighted regression models with the help of examples while demonstrating with the help of Python implementation. Traditional linear regression is a widely-used technique, but it may struggle when faced with outliers or situations where some data points carry more weight than others. However, weighted regression models help overcome these …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

Clinical Trials & Statistics Use Cases: Examples

clinical trials predictive analytics machine learning use cases

Are you a statistician, data scientist or business analyst working in the field of clinical trials? Do you find yourself curious about how statistical analyses play a pivotal role in unlocking valuable actionable insights and driving critical decisions in drug development? If so, in this blog, we will learn about various different use cases where clinical trials and statistics intersect. Clinical trials are the backbone of evidence-based medicine, paving the way for the discovery and development of innovative therapies that can improve patient outcomes. Within this realm, statistics allows researchers and analysts to make sense of complex data, evaluate treatment efficacy, assess safety profiles, and optimize trial design. In this …

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Posted in Clinical Trials, Data Science, Drug Discovery, Pharma, statistics. Tagged with , , , .

Spearman Correlation Coefficient: Formula, Examples


Have you ever wondered how you might determine the relationship between two sets of data that aren’t necessarily linear, or perhaps don’t adhere to the assumptions of other correlation measures? Enter the Spearman Rank Correlation Coefficient, a non-parametric statistic that offers robust insights into the monotonic relationship between two variables – perfect for dealing with ranked variables or exploring potential relationships in a new, exploratory dataset. In this blog post, we will learn the concepts of Spearman correlation coefficient with the help of Python code examples. Understanding the concept can prove to be very helpful for data scientists. Whether you’re exploring associations in marketing data, results from a customer satisfaction …

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Posted in Data Science, Python, statistics. Tagged with , , .

Heteroskedasticity in Regression Models: Examples


Have you ever encountered data that exhibits varying patterns of dispersion and wondered how it might impact your regression models? The varying patterns of dispersion represents the essence of heteroskedasticity – the phenomenon where the spread or variability of the residuals / errors in a regression model changes across different levels or values of the independent variables. As data scientists, understanding the concept of heteroskedasticity is crucial for robust and accurate analyses. In this blog, we delve into the intriguing world of heteroskedasticity in regression models and explore its implications through real-world examples. What’s heteroskedasticity and why learn this concept? Heteroskedasticity refers to a statistical phenomenon observed in regression analysis, …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

Underwriting & Machine Learning Models Examples

underwriting and machine learning models examples

Are you curious about how AI / machine learning is revolutionizing the underwriting process? Have you ever wondered how machine learning models are reshaping risk assessment and decision-making in industries like insurance, lending, and securities? Underwriting has long been a critical process for assessing risks and making informed decisions, but with the advent of machine learning, the possibilities have expanded exponentially. By harnessing the immense capabilities of machine learning algorithms and the abundance of data available, organizations can extract actionable insights, achieve higher accuracy, and streamline their underwriting practices like never before. In this blog, we will learn about how machine learning models can be used effectively for underwriting processes, …

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Posted in Banking, Finance, Machine Learning. Tagged with .

Loan Eligibility / Approval & Machine Learning: Examples

loan eligibility prediction using machine learning

It is no secret that the loan industry is a multi-billion dollar industry. Lenders make money by charging interest on loans, and borrowers want to get the best loan terms possible. In order to qualify for a loan, borrowers are typically required to provide information about their income, assets, and credit score. This process can be time consuming and frustrating for both lenders and borrowers. In this blog post, we will discuss how AI / machine learning can be used to predict loan eligibility. As data scientists, it is of great importance to understand some of challenges in relation to loan eligibility and how machine learning models can be built …

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Posted in AI, Banking, Finance, Machine Learning. Tagged with , , , , .