Applied Data Science Lab: Predicting Spotify Likes

Session 2: Feature Selection, Data Exploration & Analysis, Data Visualization, and Feature Engineering
Join us for the second session of our interactive, hands-on lab, where we’ll dive deeper into the essential steps of preparing data for machine learning. In this session, we’ll explore feature selection, analyze and visualize data, and apply feature engineering techniques to optimize our dataset. These foundational skills will set the stage for the next phase—implementing machine learning in Session 3.
Continuing with Spotify’s song data, we’ll uncover patterns and insights to enhance our predictive model, determining whether a song is likely to be “liked” based on its audio features.

We’ll use Python, Pandas, Matplotlib, and Scikit-learn to build our model.

Who Should Join?
These labs are designed for data enthusiasts and aspiring data scientists looking to gain hands-on experience with real-world projects. These labs are typically held over four sessions.
Prerequisites: A basic understanding of Python and Pandas will help you get the most out of the labs.
Install: Anaconda

Format:
🔹 Session 1: Project Overview and Dataset Introduction
🔹 Session 2: Exploratory Data Analysis (EDA) and Feature Engineering
🔹 Session 3: Machine Learning: Model Development and Evaluation
🔹 Session 4: Model Deployment and Final Insights

Goal of the Applied Data Science Labs
The goal of these labs is to provide a supportive, hands-on environment where learners can practice, experiment, and apply their data science knowledge with guidance from mentors and peers. Whether you're new to the field or looking to strengthen your skills, these labs offer the opportunity to work with real-world datasets and explore key areas like data wrangling, statistical analysis, data visualization, and machine learning. Through guided projects and collaborative exercises, participants can develop a portfolio that showcases their growing proficiency while building confidence in using popular data science tools.

We hope you join us!

AGENDA

• 6:00 p.m. – Welcome
• 6:05 p.m. – Review Session 1
• 6:15 p.m. – Matplotlib Review: Visualizing the Spotify Dataset
• 6:30 p.m. – Feature Selection & Exploratory Data Analysis
• 6:50 p.m. – Feature Engineering
• 7:10 p.m. – Preview of the next session
• 7:20 p.m. – Questions/Discussion
• 7:30 p.m. – End