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T1 Eda

This report outlines the need for Exploratory Data Analysis (EDA) on Geldium’s dataset to aid Tata iQ's analytics team, but the analysis could not be performed due to a corrupted or empty dataset. Consequently, no insights, missing data analysis, or risk indicators were generated, and the use of AI tools was not possible. The next step is to upload a corrected dataset to facilitate meaningful EDA and predictive modeling.

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soumyabarik771
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1K views1 page

T1 Eda

This report outlines the need for Exploratory Data Analysis (EDA) on Geldium’s dataset to aid Tata iQ's analytics team, but the analysis could not be performed due to a corrupted or empty dataset. Consequently, no insights, missing data analysis, or risk indicators were generated, and the use of AI tools was not possible. The next step is to upload a corrected dataset to facilitate meaningful EDA and predictive modeling.

Uploaded by

soumyabarik771
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Exploratory Data Analysis (EDA) Summary Report

Introduction
The purpose of this report is to perform Exploratory Data Analysis (EDA) on Geldium’s
dataset to assist Tata iQ's analytics team in understanding data quality, identifying missing
values, and surfacing early risk indicators that could influence delinquency prediction
models.

Dataset Overview
Due to a corrupted or empty dataset file, the dataset structure could not be reviewed.
Therefore, no summaries or insights could be generated for records, variables, or
anomalies.

Missing Data Analysis


Missing data analysis was not performed because the dataset could not be loaded. Once a
valid dataset is provided, missing fields can be identified and imputed using strategies such
as mean/median substitution, deletion, or synthetic generation.

Key Findings and Risk Indicators


As the dataset could not be processed, no trends, patterns, or risk indicators were extracted.
Once a valid file is available, correlations with delinquency and key variables such as credit
utilization, payment history, and income stability can be assessed.

AI & GenAI Usage


Generative AI tools such as ChatGPT were planned to be used for summarizing dataset
structures, detecting anomalies, and suggesting imputation methods. However, these tools
could not be applied due to the lack of usable data.

Conclusion & Next Steps


A corrected version of the dataset must be uploaded to enable a meaningful EDA. Once
received, the team can proceed with identifying data quality issues, modeling risk
indicators, and building predictive strategies.

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