Unlocking ML Success: Master Feature Engineering Techniques

Unlocking ML Success: Master Feature Engineering TechniquesUnlocking ML Success: Master Feature Engineering Techniques

The network for creativity

Join 1.25M professional creatives like you

Connect with clients, get discovered, and run your business 100% commission-free

Creatives on Contra have earned over $150M and we are just getting started

Back to feedPost

Sanket Sabharwal, PhD

pro

• Apr 2

Looks like the contra community is liking stuff on machine learning, and I thought it would be interesting to share another article we put together about why machine learning models can underperform for... just no obvious reason.

Despite having clean data and tuned hyperparameters, the accuracy graph barely moves. The trick here is everything related to feature engineering. We wrote a piece titled “Feature engineering decides machine learning outcomes” to share our thoughts on this quasi-"mystic" exercise.

I call it quasi-"mystic" because it is part art and part science. You have to be able to see through the rubble of messy data and know what will really move the needle for you. Much like the jeweller in our cover. It breaks down the essentials, from understanding your data’s structure to choosing well-validated features. Read the full piece here: https://www.algorithmic.co/blogs/feature-engineering-decides-machine-learning-outcomes

Machine Learning Engineering AI Engineer softwareengineer

Daniel G Bright

max

• Apr 2

Wow, this is sick. the cover as well as the article

Sanket Sabharwal, PhD

pro

• Apr 2

thanks for your support @Daniel G Bright !

Asad Aziz

• Apr 1

🚀 SMS Spam Detection Project

I’m excited to share one of my machine learning projects — an SMS Spam Detection System 📩

🔍 The goal of this project is to automatically classify incoming messages as spam or ham (legitimate messages).

💡 Key Features:

Machine Learning-based model

Text preprocessing & feature extraction

Accurate spam classification

Simple and user-friendly interface using Streamlit

🛠 Technologies Used:

Python

Scikit-learn

Pandas & NumPy

Streamlit

🌐 The project is available on GitHub and also deployed using Streamlit.

📎 Project Link: https://sms-spam-detection-001.streamlit.app/

🙏 Feel free to check it out and share your feedback. Your suggestions will help me improve further!

#MachineLearning #Python #AI #DataScience #Streamlit #GitHub #Learning

AI Development AI Engineer Python scikit-learn Streamlit Machine Learning Engineering

Marco Mastroeni

• Apr 1

Nice work! It seems really interesting. Some time ago i did something similar but for the news online. i sued textblob, nltk and vader for sentiment analysis. Did you use streamlit for the categorization of the words?

Sanket Sabharwal, PhD

pro

• Mar 30

Most teams we work with want to say their product is AI-powered. Few have asked whether their organisation is ready to support that claim. We have observed a recurring pattern where a team commits to building a machine learning system and, after months, discovers that their data isn't clean enough to train on. The infrastructure then fails to reliably serve a model. In such cases, our diagnosis has often concluded that the foundation is the problem. Machine learning readiness is a precondition, and one must begin with a straightforward question - "Do we have the data pipeline, infrastructure and cross-functional alignment to make this sustainable beyond a PoC?" That question is what prompted our first article titled "The Machine Learning Readiness Checklist". We cover everything a team needs, from data and infrastructure to the right kind of team members. All of this must be done before committing serious engineering time and budgets. If you're a founder, engineering lead or a decision maker within your organisation who wants to build things correctly, we encourage you to read the full piece here:

https://www.algorithmic.co/blogs/the-machine-learning-checklist We'll be sharing more each week. Follow along if this is relevant to what you're building.

P.S. This article is from late 2025 but more than ever relevant.

Machine Learning Engineering softwareengineering AI Engineer