From Raw Housing Data to Production-Ready Model | EDA & Feature Engineering Deep Dive

Описание: In this video, we walk through a complete, production-safe Exploratory Data Analysis (EDA) and Feature Engineering workflow using a real UK housing dataset.

This is not a surface-level tutorial.

We cover the full end-to-end process:
• Understanding dataset structure
• Quantitative vs categorical variables
• Binary, nominal and ordinal theory
• Data quality assessment (missing values, duplicates, inconsistencies)
• Target variable analysis and outlier detection
• Log transformation and distribution analysis
• Time parsing and cyclical feature engineering
• Categorical EDA (price by property type and ownership)
• Geographic feature engineering (postcode parsing)
• Rare category grouping (train-only)
• Frequency encoding (train-only)
• Target encoding with smoothing (train-only)
• Correlation matrix & multivariate analysis
• Production-safe time-based train/test split
• Feature table construction
• Scaling + Ridge regression baseline model
• Evaluation in both log space and real price (£) space

Most tutorials skip critical steps like leakage prevention, smoothing in target encoding, and realistic time-based validation.

In this video, we do it properly.

This walkthrough demonstrates how to move from raw tabular data to a clean, production-ready modeling pipeline.

Dataset: UK House Price Prediction Dataset (2015–2024)

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Who This Is For
• Data science students
• Machine learning practitioners
• Analysts transitioning to ML
• Anyone who wants to understand proper EDA beyond basic plotting

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Key Concepts Covered
• Data leakage and how to prevent it
• High-cardinality encoding strategies
• Regularization and multicollinearity
• Correlation heatmaps & feature redundancy
• Practical model evaluation

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