Boston Housing Prices EDA in Python
Explore the Boston Housing dataset with price distributions, feature correlations, and outlier detection using an AI data analyst.
Open analysis →AI Data Analysis Benchmarks for Exploratory Data Analysis
We defined practical analysis workflows from multiple domains, then ran them with AI Data Analyst using different LLM engines. On this page you can browse each workflow, open full notebook conversations, and compare model quality in shared score tables. The overall results show that modern LLMs perform very well on structured data analysis tasks.
Exploratory Data Analysis Workflow Examples
Browse reproducible AI data analysis workflows in Exploratory Data Analysis. Open any example to review prompts, conversation steps, generated code, outputs, and model-level quality scores.
E-commerce Sales Analysis in Python
Explore an e-commerce sales dataset with monthly trends, top products, category breakdowns, and average order value analysis.
Open analysis →HR Employee Attrition Analysis in Python
Explore the IBM HR Analytics dataset to uncover attrition patterns by department, age, salary, and job satisfaction.
Open analysis →Iris Species Classification with Decision Tree
Train a decision tree classifier on the Iris dataset, evaluate accuracy, and visualize the decision boundaries using an AI data analyst.
Open analysis →Titanic Survival Analysis in Python
Explore the Titanic dataset with survival rates by class, sex, and age, handle missing values, and visualize patterns using an AI data analyst.
Open analysis →Model Comparison for Exploratory Data Analysis
Compare LLM performance across workflows in this category. Open any score chip to jump directly to that model run and inspect the full conversation and notebook output.
Average score (0-10)
gemma4:31b
10.00
n=5
glm-5.1
10.00
n=5
gpt-oss:120b
10.00
n=5
qwen3-coder-next
10.00
n=5
qwen3.5:397b
9.80
n=5
gpt-5.4
9.60
n=5
gemma4:31b
Average score: 10.00/10
Scored workflows: 5
glm-5.1
Average score: 10.00/10
Scored workflows: 5
gpt-oss:120b
Average score: 10.00/10
Scored workflows: 5
qwen3-coder-next
Average score: 10.00/10
Scored workflows: 5
qwen3.5:397b
Average score: 9.80/10
Scored workflows: 5
gpt-5.4
Average score: 9.60/10
Scored workflows: 5
Detailed Workflow Comparison Table for Exploratory Data Analysis
This table compares model scores for each workflow in Exploratory Data Analysis. Open any score chip to jump directly to the selected model conversation and review full prompts, code, outputs, and score cards.
| Workflow | gemma4:31b | glm-5.1 | gpt-5.4 | gpt-oss:120b | qwen3-coder-next | qwen3.5:397b |
|---|---|---|---|---|---|---|
| Boston Housing Prices EDA in Python housing-prices-eda | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 |
| E-commerce Sales Analysis in Python ecommerce-sales-eda | 10.0/10 | 10.0/10 | 8.0/10 | 10.0/10 | 10.0/10 | 9.0/10 |
| HR Employee Attrition Analysis in Python hr-attrition-analysis | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 |
| Iris Species Classification with Decision Tree iris-classification | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 |
| Titanic Survival Analysis in Python titanic-survival-analysis | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 | 10.0/10 |
What This Benchmark Shows
We tested the same step-by-step data analysis workflows across multiple LLM models and compared results using a shared scoring rubric. In Exploratory Data Analysis, most models produce strong notebook outputs with high task completion and useful analytical reasoning. Use these examples as a reference for prompt design, model selection, and workflow quality before running similar analyses on your own data in MLJAR Studio.
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