Essential Workshop to Exploratory Data Analysis and Feature Engineering

Dive into a comprehensive 3-hour workshop on exploratory data analysis and feature engineering, led by Vladimir Rybakov, Head of Data Science at WaveAccess, and Aleksandr Mester, Data Scientist at WaveAccess. Learn effective techniques for handling real-world data challenges such as noise, missing values, and excessive information using a public Google Play Store dataset. Begin by exploring and preprocessing data, including cleaning, error fixing, and type conversion. Analyze data correlations, relationships, and variable distributions in depth. Proceed to eliminate less useful features and engineer new ones. Finally, train various machine learning models to observe how data processing and feature engineering impact model accuracy and training time. Gain hands-on experience using Python and basic machine learning concepts, with all solutions provided during the workshop. Utilize your own computer and ensure VPN restrictions don't block access to Google Colab for this practical, skill-enhancing session.