Introduction
Predict chart-topping hits with vibe coding
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Setting up your environment
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1. Importing Data
Loading data with pandas and PyArrow
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Loading data with Polars
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Reading data from HTML tables
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Generating SQL
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Merging data
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2. Data Exploration and Wrangling
Generating descriptive statistics
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Converting data types
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Examining correlations
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Aggregating data
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Reshaping data
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Creating derived variables
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A human’s job: Knowing your data is ready
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3. Data Pipelines
Feature scaling and imputing data for scikit-learn
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Encoding categorical variables
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Partitioning data
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Creating data configurations for PyTorch Tabular
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4. Model Creation and Testing
Testing different baseline models
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Tuning models
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Creating a neural network with PyTorch Tabular
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A human’s job: Evaluating performance
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5. Visualizations and Dashboards
Creating plots
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Creating explainer plots
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A human’s job: Generating insight
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Building your model in a Streamlit application
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Deploying your model in a Streamlit application
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Wrapping Up
Security considerations
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Encouragement and caution
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