Learn to build and evaluate a collaborative filtering recommender system using real-world user ratings data for personalized book recommendations.

A hands-on exploratory data analysis (EDA) project using a global cost of living dataset. Learners will investigate, clean, and interpret cost indices for cities worldwide, uncovering economic patterns and insights that inform real-world decisions about affordability and global mobility.

A hands-on guided project where you will analyze the IBM Telco Customer Churn dataset, build a Decision Tree classifier, and extract actionable business insights to reduce churn and increase customer lifetime value.

A hands-on guided project for beginner to intermediate Python learners to analyze the YouTube Trending Video Statistics dataset. Students will clean, explore, and engineer features from real-world social media data, focusing on the relationship between text features and video virality.

Embark on a hands-on data science adventure in the Pokémon universe! Analyze Pokémon stats, engineer features, and build a machine learning model to predict Legendary status. This project guides you through real-world data science workflows using pandas, seaborn, and scikit-learn, all while exploring the mysteries of Pokémon power.

A hands-on project guiding learners through exploratory data analysis and visualization of a city's restaurant market using a real-world dataset. Students will clean data, analyze cuisine and price segments, compare cities, and uncover business opportunities in the HoReCa industry.