AI-Powered Analysis of Sample Dataset
A practical and beginner-friendly guide to data analysis with Python and AI. Learn how to process, visualize, and interpret data step by step using real-world examples and tools.
This notebook will guide you through using Python and AI to data analysis step by step. You don’t need any prior experience - just follow along. Click button below each step to create Python code.
You’ll learn how to:
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Load a sample dataset
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Explore it with an easy report
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Ask AI questions about your data
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Make interactive charts
Let’s have fun with data!
Step 1 - Load a Sample Dataset
Pick one of our ready-to-go datasets to get started. Just click yellow Recipe button. The Iris dataset is a great choice to start with:
- 150 samples of three iris species (setosa, versicolor, virginica)
- 4 features: sepal length, sepal width, petal length, petal width
Or, simply click Ask AI and say Load sample dataset (or any other from scikit-learn
) to have the AI fetch it for you.
Step 2 - Automated Exploratory Data Analysis (EDA)
Run a full EDA report in one click to:
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See summary statistics (mean, median, quartiles)
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Visualize distributions and correlations
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Spot missing values and outliers
You’ll get an interactive report right here - just run the cell below and explore the tabs!
Step 3 - Ask AI for Data Insights
Got questions? Use plain English to dig deeper. For example:
“What are the strongest correlations in this dataset?”
“Are there any surprising outliers?”
Just click Ask AI: Get insights about dataset and let the AI do the rest. Feel free to tweak the prompt or ask follow-up questions in chat!
Step 4 - Build Interactive Visualizations with AI
Turn your insights into clear, interactive charts with Plotly. Try commands like:
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“Show me a scatter plot of petal length vs. petal width.”
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“Plot the distribution of sepal length by species.”
Click Ask AI: Create interactive visualization, wait a few seconds, and voilà - beautiful, interactive plots you can zoom, hover, and share!
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