--- title: "#feature-engineering articles | Hashnode" description: "Explore 102 articles tagged #feature-engineering on Hashnode. Tutorials, guides, and how-tos from the community." url: "https://hashnode.com/tag/feature-engineering" --- Tag # #feature-engineering 102 posts · 2 followers [Bahati Brenda Kizito](https://hashnode.com/@Lioness256) · [Oct 10](https://hashnode.com/posts/i-finally-understand-feature-engineering/6acdf1f55883ae173fd0f3da) ### [Your ML Model Might Need Better Features, Not a Better Algorithm Feature Engineering Explained: A Beginner's Guide](https://godfident-data.hashnode.dev/i-finally-understand-feature-engineering?utm_source=hashnode&utm_medium=feed) Learn feature engineering. Explore numerical, categorical, time and text features, feature scaling, selection, and data leakage. 16 min read · [godfident-data.hashnode.dev](https://godfident-data.hashnode.dev/i-finally-understand-feature-engineering?utm_source=hashnode&utm_medium=feed) [Vijay Ram Enaganti](https://hashnode.com/@vijay-ram-enag) · [Aug 31](https://hashnode.com/posts/sequential-attention/6a958669cc91e42ff4ace729) ### [Reproducing Sequential Attention](https://vijay-ram.hashnode.dev/sequential-attention?utm_source=hashnode&utm_medium=feed) (and finding out where my reproduction disagrees with Google's) 9 min read · [vijay-ram.hashnode.dev](https://vijay-ram.hashnode.dev/sequential-attention?utm_source=hashnode&utm_medium=feed) [Tharun](https://hashnode.com/@tharun-pandya) · [Aug 22](https://hashnode.com/posts/polynomial-regression-teaching-a-line-to-bend/6a8a080dea390438fc5306b1) ### [Polynomial Regression: Teaching a Line to Bend](https://tharunpandya.hashnode.dev/polynomial-regression-teaching-a-line-to-bend?utm_source=hashnode&utm_medium=feed) Learn polynomial regression from scratch using a delivery-time example, x^2, feature engineering, and gradient descent to fit curved patterns. 10 min read · [tharunpandya.hashnode.dev](https://tharunpandya.hashnode.dev/polynomial-regression-teaching-a-line-to-bend?utm_source=hashnode&utm_medium=feed) [Nethmi Amasha](https://hashnode.com/@amasha) · [Aug 3](https://hashnode.com/posts/why-do-both-data-preprocessing-and-feature-engineering-handle-missing-values/6a7049a1a7ce9f63fd676fbe) ### [Why Do Both Data Preprocessing and Feature Engineering Handle Missing Values?](https://curiousdeveloperbyamasha.hashnode.dev/why-do-both-data-preprocessing-and-feature-engineering-handle-missing-values?utm_source=hashnode&utm_medium=feed) Discover why Data Preprocessing and Feature Engineering both handle missing values and how their purposes differ in machine learning. 4 min read · [curiousdeveloperbyamasha.hashnode.dev](https://curiousdeveloperbyamasha.hashnode.dev/why-do-both-data-preprocessing-and-feature-engineering-handle-missing-values?utm_source=hashnode&utm_medium=feed) [Sergio González Téllez](https://hashnode.com/@evandev) · [Jul 12](https://hashnode.com/posts/beyond-sql-how-a-data-science-capstone-changed-the-way-i-think-about-metrics-state-and-observability/6a53ec976b1da08c8b241147) ### [Beyond SQL: How a Data Science Capstone Changed the Way I Think About Metrics, State, and Observability](https://evankhandev.hashnode.dev/beyond-sql-how-a-data-science-capstone-changed-the-way-i-think-about-metrics-state-and-observability?utm_source=hashnode&utm_medium=feed) From SQL Queries to State Representation: Lessons from a Data Science Capstone 6 min read · [evankhandev.hashnode.dev](https://evankhandev.hashnode.dev/beyond-sql-how-a-data-science-capstone-changed-the-way-i-think-about-metrics-state-and-observability?utm_source=hashnode&utm_medium=feed) [akshat jain](https://hashnode.com/@akshat-j0in) · [Jun 25](https://hashnode.com/posts/from-1-4m-nyc-taxi-trips-to-production-ml-feature-engineering-improved-predictions-by-34-6/6a3cff0b1f1a3ea93dcea54f) ### [From 1.4M NYC Taxi Trips to Production ML: Feature Engineering Improved Predictions by 34.6%](https://akshat-ml.hashnode.dev/from-1-4m-nyc-taxi-trips-to-production-ml-feature-engineering-improved-predictions-by-34-6?utm_source=hashnode&utm_medium=feed) How thoughtful feature engineering and SHAP analysis turned 1.4 million NYC taxi trip into a production ML model with 34.6% improvement over baseline. 6 min read · [akshat-ml.hashnode.dev](https://akshat-ml.hashnode.dev/from-1-4m-nyc-taxi-trips-to-production-ml-feature-engineering-improved-predictions-by-34-6?utm_source=hashnode&utm_medium=feed) [Yejun Park](https://hashnode.com/@yejun) · [May 30](https://hashnode.com/posts/from-thousands-of-features-to-a-few-a-deep-dive-into-dimensionality-reduction/6a1acfe5fd30dc1666f44960) ### [From Thousands of Features to a Few: A Deep Dive into Dimensionality Reduction](https://yejunpark3.hashnode.dev/from-thousands-of-features-to-a-few-a-deep-dive-into-dimensionality-reduction?utm_source=hashnode&utm_medium=feed) Dimensionality reduction: why it matters, key challenges (curse of dimensionality, redundancy), and techniques to compress high-dimensional data. 13 min read · [yejunpark3.hashnode.dev](https://yejunpark3.hashnode.dev/from-thousands-of-features-to-a-few-a-deep-dive-into-dimensionality-reduction?utm_source=hashnode&utm_medium=feed) [danieljasme](https://hashnode.com/@danieljasme) · [May 27](https://hashnode.com/posts/building-a-near-perfect-claims-classifier-random-forest-vs-xgboost-on-tiktok-data-google-advanced-da-course-5/6a173136badcd8afcb157c2f) ### [Building a Near-Perfect Claims Classifier: Random Forest vs XGBoost on TikTok Data (Google Advanced DA Course 5)](https://danieljasme.hashnode.dev/building-a-near-perfect-claims-classifier-random-forest-vs-xgboost-on-tiktok-data-google-advanced-da-course-5?utm_source=hashnode&utm_medium=feed) How I built a 99.7% accurate Random Forest model to classify TikTok claims vs opinions using Python, GridSearchCV, and NLP features. 9 min read · [danieljasme.hashnode.dev](https://danieljasme.hashnode.dev/building-a-near-perfect-claims-classifier-random-forest-vs-xgboost-on-tiktok-data-google-advanced-da-course-5?utm_source=hashnode&utm_medium=feed) [Scott McMahan](https://hashnode.com/@scottdudeman69) · [May 4](https://hashnode.com/posts/why-ai-projects-struggle-in-production/69f8b31446610fd606f40141) ### [Why AI Projects Struggle in Production](https://aitransformeronline.hashnode.dev/why-ai-projects-struggle-in-production?utm_source=hashnode&utm_medium=feed) Learn how an AI feature store strategy improves consistency, reuse, and scalability across machine learning systems in production. 2 min read · [aitransformeronline.hashnode.dev](https://aitransformeronline.hashnode.dev/why-ai-projects-struggle-in-production?utm_source=hashnode&utm_medium=feed) [kingsley ebube onoh](https://hashnode.com/@kingsley-onoh) · [Apr 18](https://hashnode.com/posts/nba-scenario-engine-usage-vacuum-modeling/69e391d746d55359b76f2502) ### [Why I Split Minutes Prediction Into Two Models Instead of One](https://kingsleyonoh.hashnode.dev/nba-scenario-engine-usage-vacuum-modeling?utm_source=hashnode&utm_medium=feed) A single regression model trained on NBA game logs predicts that Joel Embiid will play 11 minutes in a game where he's listed as OUT. The… 6 min read · [kingsleyonoh.hashnode.dev](https://kingsleyonoh.hashnode.dev/nba-scenario-engine-usage-vacuum-modeling?utm_source=hashnode&utm_medium=feed)