SKShreyash Kadaminoutlierdetectionandtreatment.hashnode.dev·1d ago · 6 min readOutlier Detection and TreatmentEvery dataset has a story to tell — but sometimes a few data points shout louder than they should. These are outliers: values that stray far from the rest of your data. Handled well, they can reveal f00
NANethmi Amashaincuriousdeveloperbyamasha.hashnode.dev·2d ago · 4 min readWhy Do Both Data Preprocessing and Feature Engineering Handle Missing Values?While studying the Machine Learning Pipeline, I came across two stages that confused me: Data Preprocessing Feature Engineering At first, everything seemed clear. Data Preprocessing is responsible00
LMLakshmi Mahabaleshwarainfreecodecamp.org·Jun 4 · 15 min readHow to Preprocess Medical Images for Machine Learning – A Guide Using Chest X-RaysWorking with healthcare data introduces preprocessing challenges that go beyond those you might encounter with structured data. Some familiar techniques still apply, while others look very different o10
MRMuhammad Razainablank-slate.hashnode.dev·Jun 3 · 3 min readData Cleaning and PreprocessingData is often messy, incomplete, or inconsistent, making it unreliable for analysis and modeling. Before building complex models, we must clean and preprocess the data to ensure accuracy and efficienc10
WTwajahat tajinai-ml-datascience-from-scratch.hashnode.dev·May 13 · 11 min readBuilding CivicAI Part 1: When No Dataset Exists, You Build OneSeries: Building an AI-Powered Municipal Complaint Management System from Scratch The Problem Nobody Talks About Every ML tutorial starts the same way: "First, load your dataset." But what do you do 00
IZismail zamarehinathar-automation.hashnode.dev·May 6 · 9 min readA Practical Guide to Tackling Class Imbalance in Tabular DatasetsA Practical Guide to Tackling Class Imbalance in Tabular Datasets Imagine you’ve built a machine learning model to detect fraudulent credit card transactions. After weeks of feature engineering and hyperparameter tuning, your model reports 99% accura...00
VUVishal Uttam Maneinvishal-uttam-mane-dataset.hashnode.dev·Apr 30 · 5 min readHandling Imbalanced DatasetsIn real-world machine learning applications, perfectly balanced datasets are more the exception than the rule. Whether in fraud detection, medical diagnosis, anomaly detection, or cybersecurity, the n00
WTwajahat tajinai-ml-datascience-from-scratch.hashnode.dev·Apr 23 · 14 min readBuilding a Full-Stack AI-Powered Municipal Civic Issue Management System from ScratchFrom a 200-Row Dataset to a Deployed ML-Powered Platform — A Complete Developer Journey Introduction Have you ever reported a pothole to your city and never heard back? Or seen overflowing garbage bin00
AAAbstract Algorithmsinabstractalgorithms.hashnode.dev·Mar 29 · 19 min readFeature Engineering: Transforming Raw Data into ML-Ready FeaturesTLDR: 🛠️ Feature engineering transforms messy real-world data into ML-compatible input. Bad features break even the best models — good features make simple algorithms shine. This guide covers scaling00
HUHabib Ur Rehmaninhabib-learned.hashnode.dev·Feb 18 · 35 min readStop Feeding Your Model Junk: The Complete Guide to Feature Selection in Machine LearningWho is this for? Anyone who wants to deeply understand feature selection — not just memorize techniques, but truly know when, why, and how to apply each one. By the end, you will look at any dataset and confidently choose the right approach. Table ...00
