Essential Workshop to Exploratory Data Analysis and Feature Engineering
Most experienced data scientists would agree that data processing takes most of the 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. 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 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 the model efficiency. Therefore, the workshop will cover:
- Primary data analysis and Preprocessing
- Exploratory Data Analysis
- Feature Engineering
Session Themen#Machine Learning
Schwierigkeitslevel: Level 2 von 5 (Einsteiger bis Fortgeschritten)
The workshop requires participants to have a basic knowledge of Python and Machine Learning. If you do not have any coding experience you are still welcome to join us to get a sense of how much effort is invested to develop a machine learning project. Also, all the solutions will be provided during the workshop. Each participant should bring their own laptop and make sure VPN restrictions do not block the connection to Google Colab.