Introduction
Tired of going through mountains of data analysis without any real information? 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 analytics and transform the way you do business.
ChatGPT: unlocking the potential of artificial intelligence for human-like conversation
Why are prompts critical in ChatGPT?
I realized that prompts are very important to use ChatGPT to its full potential. Although ChatGPT is capable of performing any task, in order to use it to its full extent, we need to provide the correct and detailed directions. Without exact directions, you will not be able to get the desired results.
I’m running the experiment to see if ChatGPT can actually make sense from the dataset. I know that ChatGPT can provide me with code snippets of certain tasks.
For example, to the message “help me with the code snippet to check for outliers”. ChatGPT provided me with a code snippet to check and identify outliers. But can a ChatGPT help me answer questions like determining the columns that contain outliers in the data set? or what is the correlation coefficient between the target variable and the characteristics?
To answer these questions, ChatGPT has to analyze the specific columns in the dataset and do the math to arrive at the answer.
Fingers crossed!
But it’s very interesting to see if ChatGPT can do the math and give me the exact answers to the questions. We’ll see!
Exploratory Data Analysis (EDA) using ChatGPT
Let’s try some of the hints, EDA using ChatGPT:
Notice 1:
I want you to act as a data scientist and analyze the data set. Give me the exact and definitive answer for each question. Do not provide me with the code snippets for the questions. The data set is provided below. Consider the given data set for the analysis. The first row of the dataset contains the header.
Hint 2:
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Patch,Ticket,Fare,Cabin,Boarded
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 Loan”,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
Hint 3:
How many rows and columns are in the data set?
Hint 4:
List the numeric and categorical columns
Hint 5:
Looking for NANs present in the dataset? If yes, write no. of nans in each column.
Hint 6:
Are there outliers in the data set?
Question 7:
Name the columns that contain the outliers. Give me the exact answer.
Hint 8:
What are the significant factors affecting the survival rate?
Hint 9:
Determine the columns that follow the skewed distribution and name them.
Hint 10:
Generate meaningful information about the data set.
So cool stuff 🙂 As you can see here, ChatGPT provided me with a summary of valuable information and also the important factors that could have affected the survival rate.
Conclusion
Awesome! ChatGPT can generate meaningful information in a very short time. My experiment is successful. And ChatGPT lived up to my expectations.
In this blog post, we have seen how to analyze data in fractions of seconds using ChatGPT. We also understood the importance of prompts in ChatGPT and the correct prompts to achieve exploratory data analysis (EDA) results. Why not try some of the directions and share your experience with me in the comments below?
I hope you enjoyed reading the article. How have you been experimenting with generative AI tools? Let me know your thoughts in the comments section below.
Everything you need to know about ChatGPT
