All right, so welcome back! In this tutorial, we will explore how to use ChatGPT for data analysis. We will start by fetching a data set and converting it into a table. Then, we will perform some simple analysis on the data, such as grouping and sorting. Next, we will build a model to predict a target class using the data set. We will evaluate the model and interpret its results using libraries like Eli5 and SHAP.
Using ChatGPT, we can easily fetch data sets and perform various data analysis tasks. It provides a user-friendly interface to interact with the data and perform operations like grouping, sorting, and encoding. Additionally, it can build machine learning models and interpret their results.
To demonstrate these capabilities, we will use a diamond data set. We will fetch the data set and convert it into a table using ChatGPT. Then, we will perform analysis on the data, such as getting the class distribution for the 'cut' column and grouping the prices into high, medium, and low price ranges.
After analyzing the data, we will build a model to predict the 'cut' column. ChatGPT will guide us through the steps of encoding the data and selecting a machine learning algorithm. We will train the model and evaluate its performance.
Finally, we will interpret the model using libraries like Eli5 and SHAP. These libraries provide insights into how the model makes predictions and which features are most important.
In conclusion, ChatGPT is a powerful tool for data analysis. It allows us to fetch data sets, perform analysis, build models, and interpret their results. With its user-friendly interface and intuitive guidance, ChatGPT makes data analysis accessible to everyone. So, give it a try and see what you can do with ChatGPT!
