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

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

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) ⸻ Who This Is For • Data science students • Machine learning practitioners • Analysts transitioning to ML • Anyone who wants to understand proper EDA beyond basic plotting ⸻ Key Concepts Covered • Data leakage and how to prevent it • High-cardinality encoding strategies • Regularization and multicollinearity • Correlation heatmaps & feature redundancy • Practical model evaluation ⸻ If you found this helpful, consider subscribing for more in-depth data science walkthroughs. #exploratorydataanalysis, #featureengineering, #datasciencetutorial, #machinelearningpython, #edapython, #targetencoding, #frequencyencoding, #dataleakage, #ridgeregression, #housingpriceprediction, #multivariateanalysis, #correlation, #programming #machinelearning