From CSV to Full Analytical Report with ChatGPT in 5 Easy Steps

From CSV to Complete Analytical Report with ChatGPT in 5 Simple Steps
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It doesn’t matter what enterprise you might be in, realizing how one can analyze information is extra essential than ever within the data-driven period. Knowledge evaluation would allow companies to remain aggressive and supply the power to make higher choices.

The significance of knowledge evaluation drives each particular person to know how one can carry out information evaluation. Nevertheless, generally it takes an excessive amount of time to conduct information evaluation. That’s why we may depend on ChatGPT to create an entire report from our information file.

This text will discover 5 easy steps to create full analytical experiences out of your CSV file. These 5 steps embody:

Step 1: Importing the CSV File

Step 2: Knowledge Abstract and Preprocessing

Step 3: Knowledge Evaluation

Step 4: Knowledge Visualization

Step 5: Report Technology

As a prerequisite, this text assumes that the reader has subscribed to ChatGPT Plus. With that in thoughts, let’s get began.



Step one is to organize the CSV file that you just need to analyze and create a report. This file may come from any reliable supply, however this text would use the Telecom Churn dataset from Kaggle.

Guarantee the information is structured, organized, and has a transparent header. When you’ve got a sure goal that you just need to analyze, then don’t neglect to incorporate that information within the file. In our instance, we might attempt to carry out an evaluation for the churn column.

With the information prepared, connect them to the ChatGPT and let the mannequin do their work.



The subsequent step is to get the information abstract from our CSV file and preprocess them. Enter the next immediate whilst you add the information.

Present the information abstract to this file and carry out acceptable information processing for the dataset.


The above immediate supplies the next outcomes:

Here is the abstract of your dataset:

Variety of Rows: 3333

Variety of Columns: 11

Column Names:












Knowledge Sorts:

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

Float: DataUsage, DayMins, MonthlyCharge, OverageFee, RoamMins

Lacking Values: There are not any lacking values in any of the columns.

ChatGPT clearly may learn the information effectively and was capable of present the given dataset. The subsequent result’s the information preprocessing from the ChatGPT.

Since there are not any lacking values within the dataset, the primary focus of knowledge cleansing and preprocessing will likely be on:

  • Checking for any outliers or anomalies within the information.
  • Normalizing or standardizing the information if required, particularly for columns with steady numerical values.
  • Changing categorical information right into a format appropriate for evaluation, if vital.

Let’s proceed with these steps. 

From CSV to Complete Analytical Report with ChatGPT in 5 Simple Steps


The boxplots present a visible overview of potential outliers in every numerical column of your dataset. Listed here are some observations:

  • AccountWeeks, CustServCalls, DayCalls: These columns present a reasonably regular distribution with just a few outliers.
  • DataUsage, DayMins, MonthlyCharge, OverageFee, RoamMins: These columns additionally present some outliers, however they appear to be inside an affordable vary, contemplating the character of the information.
  • Churn, ContractRenewal, DataPlan: Being categorical/binary information, these columns do not present conventional outliers.

Given this data, the following steps in information cleansing and preprocessing may embody:

  • Dealing with Outliers: We will resolve whether or not to take away, cap, or in any other case deal with outliers based mostly in your particular analytical wants and area data.
  • Knowledge Transformation: If wanted, we are able to normalize or standardize columns like DayMins, MonthlyCharge, and so forth., particularly in the event you plan to make use of algorithms which might be delicate to the size of knowledge.
  • Categorical Knowledge Encoding: Convert categorical information right into a format appropriate for evaluation, if any such transformation is required.

You’ll be able to let the ChatGPT carry out further steps for the information cleansing and preprocessing or let it’s as it’s. For this instance, we might proceed to the following steps.



Subsequent, we might carry out information evaluation with ChatGPT. The information evaluation is relying on what you need to obtain. In our case, we need to carry out information evaluation from the Churn POV.

We use the next immediate to execute the information evaluation with ChatGPT for the above case.

Please carry out the information evaluation based mostly on the information above with respect to the churn information.


The information evaluation may be seen within the following consequence.

To carry out information evaluation with respect to the churn information, we’ll give attention to understanding how various factors relate to buyer churn. Churn, being a binary variable (0 for no churn, 1 for churn), is usually analyzed by way of classification strategies and by inspecting relationships with different variables. Here is the plan:

  1. Correlation Evaluation: To begin, we’ll take a look at how every numerical function correlates with churn. This offers us an preliminary understanding of which components is likely to be extra carefully associated to churn.
  1. Statistical Assessments: For categorical variables like ContractRenewal and DataPlan, we are able to use statistical assessments to see if there are vital variations in churn charges throughout completely different classes.
  1. Visible Evaluation: We’ll create some visualizations to additional discover these relationships. This consists of evaluating churn charges throughout completely different teams and plotting key variables towards churn.
  1. Abstract Statistics: We’ll present abstract statistics for various teams based mostly on churn standing to see if there are noticeable variations in means or distributions.

Let’s begin with the correlation evaluation after which transfer on to the following steps.

Correlation Evaluation with Churn

The correlation of various variables with churn is as follows (from strongest destructive to strongest optimistic correlation):

Destructive Correlation:

  • ContractRenewal: Strongest destructive correlation. Signifies that prospects who don’t renew their contracts usually tend to churn.
  • DataPlan: Prospects and not using a information plan are barely extra prone to churn.
  • DataUsage: Larger information utilization barely decreases the probability of churn.

Constructive Correlation:

  • AccountWeeks, DayCalls: Very weak optimistic correlation, suggesting minimal affect on churn.
  • RoamMins: A slight improve in churn probability with elevated roaming minutes.
  • MonthlyCharge: Barely increased churn charges with elevated month-to-month expenses.
  • OverageFee: Larger overage charges are reasonably correlated with elevated churn.
  • DayMins: Larger every day minutes are reasonably correlated with elevated churn.
  • CustServCalls: Robust optimistic correlation. Extra customer support calls are related to increased churn charges.

Primarily based on these correlations, it appears that evidently components like contract renewal, customer support calls, every day minutes, and overage charges are extra carefully associated to churn. We will additional discover these components by way of visible evaluation and statistical assessments, particularly for categorical variables like ContractRenewal and DataPlan.

Visible Evaluation


From CSV to Complete Analytical Report with ChatGPT in 5 Simple Steps


The visualizations supply useful insights into the connection between completely different variables and churn:

Churn by ContractRenewal and DataPlan:

  • ContractRenewal: A big distinction in churn charges is obvious. Prospects who didn’t renew their contracts present a better fee of churn.
  • DataPlan: There’s a noticeable distinction in churn charges based mostly on whether or not prospects have an information plan or not, with these and not using a information plan displaying increased churn.

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

  • CustServCalls: Prospects with increased numbers of customer support calls are likely to churn extra.
  • DayMins: Larger every day minutes are related to a larger probability of churn.
  • MonthlyCharge: Larger month-to-month expenses correlate with an elevated churn fee.
  • OverageFee: Larger overage charges are additionally related to elevated churn.

Conclusions from the Evaluation

  • Contract renewal standing and the presence of an information plan are vital components in buyer churn.
  • Excessive utilization (by way of day minutes) and excessive expenses (month-to-month expenses and overage charges) are related to increased churn charges.
  • The next variety of customer support calls is a robust indicator of potential churn.

Subsequent Steps

Primarily based on these insights, focused methods may be developed to cut back churn, akin to bettering customer support, revising pricing methods, or providing incentives for contract renewal and information plan subscriptions.

With a single immediate, we are able to see how effectively ChatGPT may give us the whole information evaluation and provide you with perception from the information. You’ll be able to tweak the evaluation goals and begin from there.



The information visualization is a supportive exercise to enhance the information evaluation and general report. By having the visualization, we are able to have a extra complete report to grasp our information.

Utilizing the next immediate, we are able to carry out the information visualization with ChatGPT.

Please carry out information visualization from the information evaluation to clarify the information higher.


To boost the understanding of the information evaluation outcomes, I will create a collection of visualizations that clearly depict the important thing findings associated to buyer churn. These visualizations will embody:

  1. Bar Plots for Categorical Variables:
  • Evaluating churn charges for patrons with and with out contract renewal.
  • Evaluating churn charges for patrons with and and not using a information plan.
  • Field Plots for Key Numerical Variables:
  1. Buyer Service Calls vs Churn.
  • Day by day Minutes vs Churn.
  • Month-to-month Expenses vs Churn.
  • Overage Charges vs Churn.
  1. Heatmap of Correlation:

To visualise the correlation of all variables with churn.


From CSV to Complete Analytical Report with ChatGPT in 5 Simple Steps


The visualization would present all of the essential plots that encapsulate the entire dataset.  We will ask for extra detailed descriptions for every plot, which you’ll attempt independently.



The final step is to generate the report based mostly on the earlier steps. Sadly, ChatGPT may not seize all the outline and perception from the information evaluation, however we are able to nonetheless have the easy model of the report.

Use the next immediate to generate a PDF report based mostly on the earlier evaluation.

Please present me with the pdf report from step one to the final step.


You’re going to get the PDF hyperlink consequence along with your earlier evaluation coated. Attempt to iterate the steps in the event you really feel the result’s insufficient or if there are stuff you need to change.



Knowledge evaluation is an exercise that everybody ought to know because it’s one of the required expertise within the present period. Nevertheless, studying about performing information evaluation may take a very long time. With ChatGPT, we are able to reduce all that exercise time. 

On this article, we’ve mentioned how one can generate an entire analytical report from CSV recordsdata in 5 steps. ChatGPT supplies customers with end-to-end information evaluation exercise, from importing the file to producing the report.

Cornellius Yudha Wijaya is an information science assistant supervisor and information author. Whereas working full-time at Allianz Indonesia, he likes to share Python and Knowledge ideas by way of social media and writing media.

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