Analyzing Data Made Effortless Using ChatGPT

Introduction

Tired of sifting through mountains of analyzing data without any real insights? ChatGPT is here to change the game. With its advanced natural language processing capabilities, ChatGPT can uncover hidden patterns and trends in your data that you never thought possible. In this blog post, we’ll explore how ChatGPT can revolutionize your data with exploratory data analysis and transform the way you do business.

Why Prompts are Critical in ChatGPT?

I realized that prompts are very critical in order to make use of ChatGPT to its full potential. Even Though ChatGPT is capable of performing any task, in order to make use of it to its full extent, we need to provide the right and detailed prompts. Without the exact prompts, you will not be able to get the desired results.

I am running the experiment to see if ChatGPT can really make sense out of the dataset. I know that ChatGPT can provide me with the code snippets of certain tasks.

For example, given a prompt “help me with the code snippet to check for outliers”. ChatGPT provided me with a code snippet to check and identify the outliers. But can a ChatGPT help me answer the questions such as determining the columns that contain outliers in the dataset? or what is the correlation coefficient between the target variable and features?

In order to answer these questions, ChatGPT has to analyze the specific columns in the dataset and do the math to come up with the answer.

Fingers crossed!

But it’s really interesting to see if ChatGPT can do the math and provide me with the exact answers to the questions. Let’s see!

Exploratory Data Analysis (EDA) Using ChatGPT

Let’s try some of the prompts, EDA using ChatGPT:

Prompt 1:

I want you to act as a data scientist and analyze the dataset. Provide me with the exact and definitive answer for each question. Do not provide me with the code snippets for the questions. The dataset is provided below. Consider the given dataset for analysis. The first row of the dataset contains the header.

Prompt 2:

PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,”Braund, Mr. Owen Harris”,male,22,1,0,A/5 21171,7.25,,S
2,1,1,”Cumings, Mrs. John Bradley (Florence Briggs Thayer)”,female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,”Heikkinen, Miss. Laina”,female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,”Futrelle, Mrs. Jacques Heath (Lily May Peel)”,female,35,1,0,113803,53.1,C123,S
5,0,3,”Allen, Mr. William Henry”,male,35,0,0,373450,8.05,,S
6,0,3,”Moran, Mr. James”,male,,0,0,330877,8.4583,,Q

Prompt 3:

How many rows and columns are present in the dataset?

Prompt 4:

List down the numerical and categorical columns

Prompt 5:

Check for NANs present in the dataset? If yes, print no. of nans in each column.

Prompt 6:

Are there any outliers in the dataset?

Prompt 7:

Name the columns that contain the outliers. Provide me with the exact answer.

Prompt 8:

What are the significant factors that affect the survival rate?

Prompt 9:

Determine the columns that follow the skewed distribution and name them.

Prompt 10:

Generate meaningful insights about the dataset.

Such cool stuff 🙂 As you can see here, ChatGPT provided me with a summary of valuable insights and also the important factors that might have affected the survival rate.

Frequently Asked Questions

Conclusion

Impressive! ChatGPT is able to generate meaningful insights in no time. My experiment is successful. And ChatGPT lived up to my expectations.

In this blog post, we have seen how to analyze the data in fractions of seconds using ChatGPT. We also understood the importance of prompts in ChatGPT and the right prompts to achieve the results of exploratory data analysis (EDA). Why don’t you try some of the prompts and share your experience with me in the comments below?

Hope you enjoyed reading the article. How have you been experimenting with generative AI tools? Let me know your thoughts in the comment section below.