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Proud Milestones with CleanXo! Post Body: Today, I want to share some achievements in my journey building CleanXo, my automated data science platform! Here’s what I’ve accomplished so far: Designed the full vision for CleanXo: from data cleaning t

"I'm building an automated data cleaning platform, CleanXo, that simplifies the entire data preprocessing pipeline. It will automatically detect and handle outliers (Z-score, IQR, Isolation Forest), perform feature engineering, generate insights with

📊 Data Science Reality Check! Ever tracked how much time you actually spend on: ⏳ 60% Data cleaning & preprocessing 📊 20% Exploratory analysis 🤖 15% Model building 💡 5% Delivering insights The hard truth: We spend 12x more time preparing data t

Today I was sitting in my office and thinking what if we can connect the financial data of small start-ups and companies, all automated for cleaning and prediction, connected in and out providing amazing sales insight and other data related solutions

Cloud Security Services – DigitDefence Scalable. Secure. Cloud Confidence Starts Here. As your business moves to the cloud, so do the risks. DigitDefence provides advanced cloud security services that protect your infrastructure, apps, and sensitive

🟦 Part 1: Data Extraction – Starting the ETL Pipeline 🚀 Welcome to Part 1 of my Azure-based ETL project series! In this part, I walk through how to extract raw data from a GitHub link and load it into Azure Data Lake (Gen2) using Azure Data Factor

Essential Python Topics for Data Analysts 😄👇 - Data Structures: Lists, Dictionaries, Sets, and Tuples. - NumPy: Array manipulation for numerical operations. - Pandas: Data manipulation and analysis with DataFrame. - Data Cleaning: Handling missin

🚀 How I Turned a Real Problem Into a Career Opportunity After working with start-ups and freelancing, I kept seeing the same issue: messy spreadsheets slowing everyone down. 🧹 The Problem: Data cleanup is frustrating, time-consuming, and either t

A major hurdle in achieving business sustainability is the "data dilemma": poor data quality &integration, leading to flawed decision-making. Despite increasing interest in ESG, many companies struggle with scattered, inconsistent, &incomplete sustai

Machine Learning vs. Deep Learning: What’s the Real Difference? 🤖⚡ Machine Learning (ML) and Deep Learning (DL) are both AI-driven, but they’re not the same! While ML relies on algorithms to learn from data, DL uses artificial neural networks to pr