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🎭 Building a Robust Real-Time Emotion Detection System Using Ensemble Learning   🔗 GitHub Repository: https://github.com/KToppo/Emotion-Detection-ML Human emotion recognition has emerged as a powerful tool in modern AI applications—ranging from digital well-being solutions to marketing analytics and interactive systems. In this project, I built a Real-Time Emotion Detection System that uses a camera feed or an image URL to classify a person’s facial expression into one of several emotion categories. The complete project — including code, models, pipelines, and demo — is available on GitHub: 👉 https://github.com/KToppo/Emotion-Detection-ML This blog documents the entire journey — from data preprocessing to final deployment — and highlights the experiments, improvements, and insights gained along the way. 📂 Project Structure Here is the complete directory structure: ├── models/ │ ├── labels_1.pkl │ ├── labels_2.pkl │ ├── labels_3.pkl │ ├── M1SMOTE_boost.png │ ├── M1...

Recommendation systems are used in many of the apps we use every day – from Netflix suggesting movies to Amazon recommending products . As a book lover and data science enthusiast , I wanted to create a system that helps readers find new books based on what they like . In this post , I’ll walk you through how I built a Book Recommendation System using Python , Streamlit , and Collaborative Filtering .   📌 Project Overview The goal was to create a simple yet effective web app that: * Shows the Top 50 most popular books based on average ratings.   * Recommends similar books to a user - selected title using collaborative filtering.   * Provides a clean , interactive user interface built with Streamlit.   📁 Dataset I used the Book Recommendation Dataset from Kaggle, which includes : * Books.csv – Book details like title , author , and image URL * Users.csv – User demographic data * Ratings .csv – Book rat...