Scribd
Upload
7K views2 pages

EDA SummaryReport Template

The Exploratory Data Analysis (EDA) report template outlines the structure for analyzing a dataset, including an overview of the dataset, missing data analysis, and key findings related to risk indicators. It emphasizes the importance of identifying trends and patterns, as well as utilizing AI tools for data summarization and imputation. The report concludes with a summary of findings and recommended next steps for further analysis.

Uploaded by

Vineeth Vivekanandan
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
7K views2 pages

EDA SummaryReport Template

The Exploratory Data Analysis (EDA) report template outlines the structure for analyzing a dataset, including an overview of the dataset, missing data analysis, and key findings related to risk indicators. It emphasizes the importance of identifying trends and patterns, as well as utilizing AI tools for data summarization and imputation. The report concludes with a summary of findings and recommended next steps for further analysis.

Uploaded by

Vineeth Vivekanandan
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
You are on page 1/ 2

Exploratory Data Analysis (EDA) Summary

Report Template

1. Introduction
[Insert purpose and goal(s) of the report.]

2. Dataset Overview
This section summarizes the dataset, including the number of records, key variables,
and data types. It also highlights any anomalies, duplicates, or inconsistencies observed
during the initial review.

Key dataset attributes:

- Number of records: [Insert count]

- Key variables: [List key columns and descriptions]

- Data types: [Categorical, Numerical, etc.]

3. Missing Data Analysis


Identifying and addressing missing data is critical to ensuring model accuracy. This
section outlines missing values in the dataset, the approach taken to handle them, and
justifications for the chosen method.

Key missing data findings:

- Variables with missing values: [List affected columns]

- Missing data treatment: [Deletion, Imputation, Synthetic Data, etc.]

4. Key Findings and Risk Indicators


This section identifies trends and patterns that may indicate risk factors for delinquency.
Feature relationships and statistical correlations are explored to uncover insights
relevant to predictive modeling.

Key findings:

- Correlations observed between key variables: [Summarize findings]

- Unexpected anomalies: [Highlight data points requiring further investigation]


5. AI & GenAI Usage
Generative AI tools were used to summarize the dataset, impute missing data, and
detect patterns. This section documents AI-generated insights and the prompts used to
obtain results.

Example AI prompts used:

- 'Summarize key patterns in the dataset and identify anomalies.'

- 'Suggest an imputation strategy for missing income values based on industry best
practices.'

6. Conclusion & Next Steps


[Summarize key findings and outline the recommended next steps.]

You might also like