Machine Learning

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Without k-Anonymity, a unique individual may stand out within a dataset, increasing the risk of re-identification. After applying k-Anonymity, records become part of a larger group, making it more difficult to distinguish any single individual.

The Basics of k-Anonymity: Making Individuals Harder to Identify

k-Anonymity is a privacy-preserving technique designed to reduce the risk of identifying individuals in a dataset. The central idea is simple: each record should be indistinguishable from at least k − 1 other records based on a selected set of attributes.

Swetha Srihari

Jun 56 min read

Figure 1. Data collected from everyday online activities can be used to personalize services and advertisements, highlighting the growing importance of protecting individual privacy in machine learning systems.

The Privacy Challenge in Machine Learning

I had always believed that we can maintain privacy by not revealing too much information about ourselves in online forums and social media. But my perspective changed whenI took a course during my graduate studies called “Privacy Aware Computing”. I attended the first lecture without even enrolling in the course because I wanted to know if it was something that would interest me. I was not expecting much. But I was so drawn by the subject. That single lecture was enough to c

Swetha Srihari

Jun 47 min read

From Raw Healthcare Data to Diabetes Insights: End-to-End Data Analytics Project Using Python & Tableau

In Healthcare, data is often available in large quantities with messy and inconsistent format. When I explored this dataset, I noticed missing values, inconsistent formats, outliers, and unstructured clinical measurements. This blog mainly focuses on transforming raw healthcare data into meaningful insights using Python for data cleaning and feature engineering, followed by building interactive dashboards in Tableau. The main goal of this blog, before building dashboards or

saranyashanmugam200

Jun 36 min read

Building and Deploying a Streamlit Dashboard: A Complete Step-by-Step Guide

When I started working on a diabetes dashboard data, I wanted something simple: a clean web app where I could explore data, detect outliers, visualize patterns, and run a machine learning model with a known and easy language python. Streamlit is a remarkable Python library that lets you turn a normal Python script into a fully interactive web application. In this blog, I’ll walk through everything I did — from installing packages, building the app, adding caching and navig

saranyashanmugam200

Jun 14 min read

How to build a Machine Learning Model?

Machine Learning has become one of the most talked-about topics in the industry today. From Netflix recommendations to Google search results, machine learning is everywhere. When I first started learning machine learning, I assumed it was all about complex math and confusing algorithms. But once I understood the workflow, everything started to make sense. Machine learning is not magic. It is a step-by-step process, very similar to how humans learn from experiences. Let's bre

Anandhi Kamalakannan

Jan 124 min read

Machine Learning 101

I always thought Machine learning is a complex term, something very difficult to understand for a basic programmer like me. Because of that belief, I assumed machine learning was not my genre at all. Today, I regret thinking that way. Once I started learning it, I realized that the concepts were not that scary at all, as I imagined. In fact, I was able to understand them and even relate them to real-world scenarios. I can now deeply connect the dots. For the first time, I fee

Anandhi Kamalakannan

Jan 114 min read

Data Cleaning Explained: How Clean Data Drives Better Visualizations and Decisions

Hi team! Check out my latest blog on why data cleaning is critical for accurate visualizations and better business decisions. I also show a real-world COVID-19 dataset case study using Python.

Gayathri Venkatachalam

Jan 98 min read