Data Exploration: Take Action
You may encounter outliers, and missing data during data exploration process and handling such cases is an important part of data preparation in machine learning. Here are some strategies and techniques for dealing with these issues: Outliers: Outliers are values that are significantly different from the other values in the dataset. They can arise due to measurement error, data entry errors, or other factors. Outliers can affect the performance of machine learning models, so it is importantFew readersHow to Deal With Unbalanced Datasets?
Dealing with unbalanced datasets is a common challenge in machine learning. An unbalanced dataset is one where the number of observations in each class is significantly different. For example, in a binary classification problem, if 90% of the observations belong to one class and only 10% belong to the other class, the dataset is unbalanced. In this article, we will discuss several strategies for dealing with unbalanced datasets in machine learning. An unbalanced data sample (https://storage.cFew readersChat interaction
The Chat Interaction feature in NextBrain allows you to interact with an intelligent chat bot powered by GPT (Generative Pre-trained Transformer). The chat bot has two modes: "Chat explore" and "Chat transform," each providing a different way to interact with your data. This page will guide you through using these modes effectively. Getting Started To access the Chat Interaction feature, follow these steps:Few readersData Exploration Basics
Exploring the data before training a machine learning model is a critical step in the machine learning workflow, as it helps to identify potential issues with the data that could affect the performance of the model. Here are some steps you can take to explore the data before training a machine learning model: Data visualization: Create visualizations of the data using tools to identify patterns and relationships in the data, as well as outliers and missing values. A Sample Scatter Plot atFew readers