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Harness the Power of AI for Business


FROM CSV TO COMPLETE ANALYTICAL REPORT WITH CHATGPT IN 5 SIMPLE STEPS

Data analysis is a time-consuming activity. With ChatGPT, we can perform data
summary, data preprocessing, data visualization, and many others in a short
time.

By Cornellius Yudha Wijaya, KDnuggets on November 27, 2023 in Data Science


--------------------------------------------------------------------------------





Image by rawpixel.com on Freepik
 



No matter what business you are in, knowing how to analyze data is more
important than ever in the data-driven era. Data analysis would enable
businesses to stay competitive and provide the ability to make better decisions.

The importance of data analysis drives every individual to know how to perform
data analysis. However, sometimes it takes too much time to conduct data
analysis. That’s why we could rely on ChatGPT to create a complete report from
our data file.

This article will explore five simple steps to create complete analytical
reports from your CSV file. These five steps include:



Step 1: Importing the CSV File

Step 2: Data Summary and Preprocessing

Step 3: Data Analysis

Step 4: Data Visualization

Step 5: Report Generation

As a prerequisite, this article assumes that the reader has subscribed to
ChatGPT Plus. With that in mind, let’s get started.

 


IMPORTING THE CSV FILE

 

The first step is to prepare the CSV file that you want to analyze and create a
report. This file could come from any trustworthy source, but this article would
use the Telecom Churn dataset from Kaggle.

Ensure the data is structured, organized, and has a clear header. If you have a
certain target that you want to analyze, then don’t forget to include that data
in the file. In our example, we would try to perform an analysis for the churn
column.

With the data ready, attach them to the ChatGPT and let the model do their work.

 


DATA SUMMARY AND PREPROCESSING

 

The next step is to get the data summary from our CSV file and preprocess them.
Input the following prompt while you upload the data.

Provide the data summary to this file and perform appropriate data processing for the dataset.

 

The above prompt provides the following results:

Here's the summary of your dataset:

Number of Rows: 3333

Number of Columns: 11

Column Names:

Churn

AccountWeeks

ContractRenewal

DataPlan

DataUsage

CustServCalls

DayMins

DayCalls

MonthlyCharge

OverageFee

RoamMins

Data Types:

Integer: Churn, AccountWeeks, ContractRenewal, DataPlan, CustServCalls, DayCalls

Float: DataUsage, DayMins, MonthlyCharge, OverageFee, RoamMins

Missing Values: There are no missing values in any of the columns.

ChatGPT clearly could read the data well and was able to provide the given
dataset. The next result is the data preprocessing from the ChatGPT.

Since there are no missing values in the dataset, the main focus of data
cleaning and preprocessing will be on:

 * Checking for any outliers or anomalies in the data.
 * Normalizing or standardizing the data if required, especially for columns
   with continuous numerical values.
 * Converting categorical data into a format suitable for analysis, if
   necessary.

Let's proceed with these steps. 
 



 



The boxplots provide a visual overview of potential outliers in each numerical
column of your dataset. Here are some observations:

 * AccountWeeks, CustServCalls, DayCalls: These columns show a fairly normal
   distribution with a few outliers.
 * DataUsage, DayMins, MonthlyCharge, OverageFee, RoamMins: These columns also
   show some outliers, but they seem to be within a reasonable range,
   considering the nature of the data.
 * Churn, ContractRenewal, DataPlan: Being categorical/binary data, these
   columns don't show traditional outliers.

Given this information, the next steps in data cleaning and preprocessing could
include:

 * Handling Outliers: We can decide whether to remove, cap, or otherwise handle
   outliers based on your specific analytical needs and domain knowledge.
 * Data Transformation: If needed, we can normalize or standardize columns like
   DayMins, MonthlyCharge, etc., especially if you plan to use algorithms that
   are sensitive to the scale of data.
 * Categorical Data Encoding: Convert categorical data into a format suitable
   for analysis, if any such transformation is required.

You can let the ChatGPT perform additional steps for the data cleaning and
preprocessing or let it be as it is. For this example, we would continue to the
next steps.

 


DATA ANALYSIS

 

Next, we would perform data analysis with ChatGPT. The data analysis is
depending on what you want to achieve. In our case, we want to perform data
analysis from the Churn POV.

We use the following prompt to execute the data analysis with ChatGPT for the
above case.

Please perform the data analysis based on the data above with respect to the churn data.

 

The data analysis can be seen in the following result.

To perform data analysis with respect to the churn data, we'll focus on
understanding how different factors relate to customer churn. Churn, being a
binary variable (0 for no churn, 1 for churn), is typically analyzed through
classification methods and by examining relationships with other variables.
Here's the plan:

 1. Correlation Analysis: To start, we'll look at how each numerical feature
    correlates with churn. This gives us an initial understanding of which
    factors might be more closely related to churn.

 2. Statistical Tests: For categorical variables like ContractRenewal and
    DataPlan, we can use statistical tests to see if there are significant
    differences in churn rates across different categories.

 3. Visual Analysis: We'll create some visualizations to further explore these
    relationships. This includes comparing churn rates across different groups
    and plotting key variables against churn.

 4. Summary Statistics: We'll provide summary statistics for different groups
    based on churn status to see if there are noticeable differences in means or
    distributions.

Let's start with the correlation analysis and then move on to the next steps.

Correlation Analysis with Churn

The correlation of different variables with churn is as follows (from strongest
negative to strongest positive correlation):

Negative Correlation:

 * ContractRenewal: Strongest negative correlation. Indicates that customers who
   do not renew their contracts are more likely to churn.
 * DataPlan: Customers without a data plan are slightly more likely to churn.
 * DataUsage: Higher data usage slightly decreases the likelihood of churn.

Positive Correlation:

 * AccountWeeks, DayCalls: Very weak positive correlation, suggesting minimal
   impact on churn.
 * RoamMins: A slight increase in churn likelihood with increased roaming
   minutes.
 * MonthlyCharge: Slightly higher churn rates with increased monthly charges.
 * OverageFee: Higher overage fees are moderately correlated with increased
   churn.
 * DayMins: Higher daily minutes are moderately correlated with increased churn.
 * CustServCalls: Strong positive correlation. More customer service calls are
   associated with higher churn rates.

Based on these correlations, it seems that factors like contract renewal,
customer service calls, daily minutes, and overage fees are more closely related
to churn. We can further explore these factors through visual analysis and
statistical tests, especially for categorical variables like ContractRenewal and
DataPlan.

Visual Analysis

 



 



The visualizations offer valuable insights into the relationship between
different variables and churn:

Churn by ContractRenewal and DataPlan:

 * ContractRenewal: A significant difference in churn rates is evident.
   Customers who did not renew their contracts show a higher rate of churn.
 * DataPlan: There is a noticeable difference in churn rates based on whether
   customers have a data plan or not, with those without a data plan showing
   higher churn.

Numerical Variables (CustServCalls, DayMins, MonthlyCharge, OverageFee) by
Churn:

 * CustServCalls: Customers with higher numbers of customer service calls tend
   to churn more.
 * DayMins: Higher daily minutes are associated with a greater likelihood of
   churn.
 * MonthlyCharge: Higher monthly charges correlate with an increased churn rate.
 * OverageFee: Higher overage fees are also associated with increased churn.

Conclusions from the Analysis

 * Contract renewal status and the presence of a data plan are significant
   factors in customer churn.
 * High usage (in terms of day minutes) and high charges (monthly charges and
   overage fees) are associated with higher churn rates.
 * A higher number of customer service calls is a strong indicator of potential
   churn.

Next Steps

Based on these insights, targeted strategies can be developed to reduce churn,
such as improving customer service, revising pricing strategies, or offering
incentives for contract renewal and data plan subscriptions.

With a single prompt, we can see how well ChatGPT could give us the complete
data analysis and come up with insight from the data. You can tweak the analysis
aims and start from there.

 


DATA VISUALIZATION

 

The data visualization is a supportive activity to improve the data analysis and
overall report. By having the visualization, we can have a more comprehensive
report to understand our data.

Using the following prompt, we can perform the data visualization with ChatGPT.

Please perform data visualization from the data analysis to explain the data better.

 

To enhance the understanding of the data analysis results, I'll create a series
of visualizations that clearly depict the key findings related to customer
churn. These visualizations will include:

 1. Bar Plots for Categorical Variables:

 * Comparing churn rates for customers with and without contract renewal.
 * Comparing churn rates for customers with and without a data plan.
 * Box Plots for Key Numerical Variables:

 2. Customer Service Calls vs Churn.

 * Daily Minutes vs Churn.
 * Monthly Charges vs Churn.
 * Overage Fees vs Churn.

 3. Heatmap of Correlation:

To visualize the correlation of all variables with churn.

 



 



The visualization would show all the important plots that encapsulate the whole
dataset.  We can ask for more detailed descriptions for each plot, which you can
try independently.

 


REPORT GENERATION

 

The last step is to generate the report based on the previous steps.
Unfortunately, ChatGPT might not capture all the description and insight from
the data analysis, but we can still have the simple version of the report.

Use the following prompt to generate a PDF report based on the previous
analysis.

Please provide me with the pdf report from the first step to the last step.

 

You will get the PDF link result with your previous analysis covered. Try to
iterate the steps if you feel the result is inadequate or if there are things
you want to change.

 


CONCLUSION

 

Data analysis is an activity that everyone should know as it’s one of the most
required skills in the current era. However, learning about performing data
analysis could take a long time. With ChatGPT, we can minimize all that activity
time. 

In this article, we have discussed how to generate a complete analytical report
from CSV files in 5 steps. ChatGPT provides users with end-to-end data analysis
activity, from importing the file to producing the report.
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer.
While working full-time at Allianz Indonesia, he loves to share Python and Data
tips via social media and writing media.





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