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,

Central Tendency in Machine Learning: Python Examples

central tendency machine learning python examples

Have you ever wondered why your machine learning model is not performing as expected? Could the “average” behavior of your dataset be misleading your model? How does the “central” or “typical” value of a feature influence the performance of a machine learning model? In this blog, we will explore the concept of central tendency, its significance in machine learning, and the importance of addressing skewness in your dataset. All of this will be demonstrated with the help of Python code examples using a diabetes dataset. We will be working with the diabetes dataset which can be found on Kaggle – Diabetes Dataset. The dataset consists for multiple columns such as …

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Posted in Data Science.

Feature Engineering in Machine Learning: Python Examples

feature engineering in machine learning

Have you ever wondered why some machine learning models perform exceptionally well while others don’t? Could the magic ingredient be something other than the algorithm itself? The answer is often “Yes,” and the magic ingredient is feature engineering. Good feature engineering can make or break a model. In this blog, we will demystify various techniques for feature engineering, including feature extraction, encoding categorical variables, feature scaling, and feature selection. To demonstrate these methods, we’ll be using a real-world dataset containing car sales data. This dataset includes a variety of features such as ‘Company Name’, ‘Model Name’, ‘Price’, ‘Model Year’, ‘Mileage’, and more. Through this dataset, we’ll explore how to improve …

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Posted in Machine Learning, Python. Tagged with , .

Data Analytics for Car Dealers: Actionable Insights

car dealers data analytics inventory management

Are you starting a car dealership and wondering how to leverage data to make informed business decisions? In today’s data-driven world, analytics can be the difference between a thriving business and a failing one. This blog aims to provide actionable insights for car dealers, especially those starting new car dealer business, to excel in various business aspects. I will cover inventory management, pricing strategy, marketing and sales, customer service, and risk mitigation, all backed by data analytics. I will continue to update this blog with more methods in time to come. The data used for analysis can be found on the – Ultimate Car Price Prediction Dataset. First and …

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

Unemployment Data & Actionable Insights Examples

Distribution of unemployment rates and actionable insights

Unemployment figures often flood the news, painting a broad picture of economic stability or crisis. But have you ever wondered how these rates break down at the local level? Do certain counties (or cities) in different states fare better or worse than the national average, and if so, why? Unemployment is a critical indicator of economic health and social well-being. While national or state-level unemployment rates often make headlines, diving deeper into county-level or city level data can offer valuable insights for local governments, policymakers, and social organizations. In this blog, we will explore a dataset that provides unemployment rates for various U.S. counties in June 2023. Along the way, …

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Posted in Data, Data analytics. Tagged with .

Insurance & Linear Regression Model Example

Ever wondered how insurance companies determine the premiums you pay for your health insurance? Predicting insurance premiums is more than just a numbers game—it’s a task that can impact millions of lives. In this blog, we’ll demystify this complex process by walking you through an end-to-end example of predicting health insurance premium charges by demonstrating with Python code example. Specifically, we’ll use a linear regression model to predict these charges based on various factors like age, BMI, and smoking status. Whether you’re a beginner in data science or a seasoned professional, this blog will offer valuable insights into building and evaluating regression models. What is Linear Regression? Linear Regression is …

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Posted in Data Science, Insurance, Machine Learning, statistics. Tagged with , , .

Chi-square test – Formula, Concepts, Examples

chi-square test for test of independence

The Pearson’s Chi-square (χ2) test is a statistical test used to determine whether the distribution of observed data is consistent with the distribution of data expected under a particular hypothesis. The Chi-square test can be used to compare or evaluate the independence of two distributions, or to assess the goodness of fit of a given distribution to observed data. In this blog post, we will discuss different types of Chi-square tests, the concepts behind them, and how to perform them using Python / R. As data scientists, it is important to have a strong understanding of the Chi-square test so that we can use it to make informed decisions about …

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

Text Clustering Real-World Applications: Examples

Text Clustering Real World Applications and Examples

How often have you wondered about the vast amounts of unstructured data around us and its untapped potential? How can businesses sift through thousands of customer reviews, documents, or feedback to derive actionable insights? What if there was a way to automatically group similar pieces of text, helping organizations quickly identify patterns and trends? Enter text clustering. A subset of text analytics, text clustering is an unsupervised machine learning task that divides a set of texts into clusters or groups. This ensures that texts in the same group are more similar to each other than to those in other groups. A powerful tool for deciphering insights from unstructured data, text …

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Posted in Machine Learning, NLP. Tagged with , .

Problems, Symptoms & Root Cause Analysis (RCA) Examples

Process for identifying problem and doing root cause analysis

Have you ever found yourself stuck in a cycle of solving the same problems over and over again? Ever wondered why some solutions seem to only offer a temporary fix? Have you ever wondered if you have identified the correct problem or you are trying to fix one of the symptoms? The key lies in your understanding of problem, symptoms and root cause, and approach to problem-solving, which is fundamentally rooted in analytical thinking. What exactly is the difference between a problem and its symptoms? And why is it crucial to conduct a root cause analysis to arrive at a lasting solution? In both personal and professional spheres (work place), …

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Posted in Analytics, Problem Solving. Tagged with , .

Contract Analysis & Review Checklist: Questions, Examples

contract review checklist

Have you ever found yourself knee-deep in contractual jargon, wondering if you’ve missed a critical clause that could cost your organization thousands or even millions? How confident are you that every contract your team signs is optimized for both performance and cost efficiency? If you’re a procurement stakeholder, a category manager, or a contract specialist, these questions are not just hypothetical—they’re the daily challenges you face. In this blog, you will learn about a structured approach to learning, understanding, and reviewing contracts, minimizing risks, and maximizing value based on asking the right kind of questions. We delve into key questions you should be asking, highlight essential clauses to scrutinize, and …

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Posted in Data analytics, Procurement. Tagged with .

Linear Regression Python Examples

SSR, SSE and SST Representation in relation to Linear Regression

In this post, you will learn about concepts of linear regression along with Python Sklearn examples for training linear regression models. Linear regression belongs to a class of parametric models and is used to train supervised machine learning models.  Introduction to Linear Regression Linear regression is a machine learning algorithm used to predict the value of continuous response variables. The predictive analytics problems that are solved using linear regression models are called supervised learning problems as they require that the value of response/target variables must be present and used for training the models. Also, recall that “continuous” represents the fact that the response variable is numerical in nature and can take infinite different values. …

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

Find Topics of Text Clustering: Python Examples

Finding topics for text clusters using Python

Have you ever clustered a collection of texts and wondered what predominant topics underlie each group? How can you pinpoint the essence of each cluster comprising of large volume of words? Is there a way to succinctly represent the core topic of each cluster using Python? Text clustering is a powerful technique in natural language processing (NLP) that groups documents into clusters based on their content. Once you’ve clustered your data, a natural follow-up question arises: “What are these clusters about?” In this article, we’ll discuss two different methods to find the dominant topics of text clusters using Python. Meanwhile, check out my post on text clustering – Text Clustering …

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Posted in Machine Learning, NLP, Python. Tagged with , , .

Productivity vs Efficiency: Differences, Examples

productivity vs efficiency matrix 1

If you’ve ever found yourself caught in the whirlwind of tasks and deadlines, you’ve probably asked yourself: “How can I get more done?” or “How can I make better use of my time?” At the core of these questions lie two concepts that are often used interchangeably but are fundamentally different: Productivity and Efficiency. Understanding the nuances between productivity and efficiency can be a game-changer in both your personal and professional life. While both are geared towards improving performance and achieving goals, they focus on different aspects of the work process. Knowing when to prioritize one over the other can mean the difference between spinning your wheels and skyrocketing your …

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Posted in Problem Solving. Tagged with .

6 Brainstorming Techniques for Generating Great Ideas

Mind mapping brainstorming ideas

Generating innovative and creative ideas is a key component of success in many fields, from business and marketing to science, technology, and the arts. However, the process of coming up with new and unique ideas can be challenging, especially when faced with deadlines, limited resources, or creative blocks. Fortunately, there are several effective brainstorming techniques that can help individuals and teams generate great ideas and overcome obstacles to innovation. When it comes to generating great ideas, brainstorming is one of the most effective techniques out there. But not all brainstorming sessions are created equal. In order for a brainstorming session to be successful, you need to use the right techniques. …

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OpenAI Python API Example for NLP Tasks

OpenAI Python API Example

Ever wondered how you can leverage the power of OpenAI’s GPT-3 and GPT-3.5 (from Jan 2024 onwards) directly in your Python application? Are you curious about generating human-like text with just a few lines of code? This blog post will walk you through an example Python code snippet that utilizes OpenAI’s Python API for different NLP tasks such as text generation. Check out my other post on how to use Langchain framework for text generation using OpenAI GPT models. OpenAI Python APIs The OpenAI Python API is an interface that allows you to interact with OpenAI’s language models, including their GPT-3 model. The following are different popular models that you …

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Posted in Generative AI, Machine Learning, NLP, OpenAI, Python. Tagged with , , , .

Procurement Advanced Analytics Use Cases

procurement analytics use cases

The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organizations, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts/product managers, and data scientists to implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision-making.  Procurement analytics use cases can be initiated by utilizing dashboards to …

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

Architecting a Generative AI Platform for GPT-based LLM Apps

Generative AI Platform Architecture for OpenAI GPT based LLM Apps

Have you ever wondered how to build a scalable Generative AI platform based on OpenAI GPT models that can serve different applications? Are you a data scientist, product manager, or software engineer looking to understand the intricacies of the architecture of such a scalable generative AI platform? This blog aims to demystify the architectural building blocks needed to create a robust GPT-based platform. By the end, you will have a clear roadmap for architecting, designing, and implementing your own GPT-based large language models (LLMs) applications platform. Generative AI Platform Architecture for GPT-based LLM Apps The following is the technology architecture of generative AI platform which can leverage OpenAI GPT based …

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Posted in Generative AI, Machine Learning, OpenAI. Tagged with , , .