Data Analysis Workshop
Most experienced data scientists would agree that data processing takes the most time when undertaking machine learning projects. Both data pre-processing and feature engineering quality is crucial for model performance. However, it is not typically an easy thing to do. When dealing with real data, you are likely to encounter such problems as noise, missing values, excessive information, etc. Building a good feature vector turns out to be just as hard. In this workshop, you will learn some simple but effective ways of handling these problems. First of all, we’ll explore and preprocess the data: clean them, fix the errors, convert to the appropriate type, etc. Then we will analyze data relations. After that, we will use several ways to engineer new features. Finally, we will show how feature engineering affects model efficiency. Therefore, the workshop will cover: Primary data analysis and Preprocessing Exploratory Data AnalysisFeature EngineeringData Analysis and Feature engineering tools (new).