Lesson 1Feature scaling and transformation: log transforms for skewed revenue/quantity, robust scalingUse scaling and changes to steady variation and cut skew in revenue and amounts, with log changes, tough scaling, and power shifts while keeping meaning clear.
Diagnosing skewness and heavy tailsLog and power transformationsStandard, min-max, and robust scalingScaling pipelines with sklearnInverse transforms for interpretationLesson 2Datetime feature engineering: weekday, hour, seasonality, recency and tenure features from order_date and customer historyBuild time features from order dates and customer past, like days of week, hours, seasons, how recent, and how long known, keeping time order to stop leaks in forecasts and sorting tasks.
Extracting calendar-based featuresCyclic encoding of time variablesSeasonality and holiday indicatorsRecency and tenure feature designTime-aware leakage preventionLesson 3Imputation strategies for numeric (median, KNN, model-based) and categorical fields (mode, 'unknown')Compare filling methods for numbers and categories, like middle values, nearest neighbours, model fills, most common, and 'unknown' tags, checking for shifts, spread, and strength.
Missingness mechanisms and patternsSimple numeric imputation methodsKNN and model-based imputationCategorical mode and "unknown" binsUsing missingness indicator flagsLesson 4Creating target variable for chosen prediction (binary returned, continuous revenue, late delivery label)Set up target values for main predictions, like yes/no returns, steady revenue, and late delivery tags, with clear meanings matching check measures.
Choosing the prediction objectiveDefining return and churn labelsRevenue and margin regression targetsLate delivery and SLA breach labelsAligning targets with metricsLesson 5Encoding techniques: one-hot, target encoding, frequency encoding, embeddings for high-cardinality featuresCheck ways to code categories, from basic one-hot to target, count, and embed codes, with tips on stopping leaks, steadying, and big category handling.
When to use one-hot encodingTarget encoding with leakage controlFrequency and count encodingsHashing and rare category handlingLearned embeddings for categoriesLesson 6Outlier detection and handling for price, quantity, delivery_time_days, and revenueSpot, check, and fix odd values in price, amounts, delivery days, and revenue using stats and business sense, keeping data while guarding models from shakes.
Univariate outlier detection rulesMultivariate and contextual outliersCapping, trimming, and winsorizationBusiness-rule based outlier flagsImpact of outliers on model trainingLesson 7Aggregations and customer-level features: historical return rate, avg order value, frequency, time since last orderMake customer summaries like past return rates, average order worth, buy frequency, and time since last buy to catch long-term habits and boost grouping and predictions.
Customer-level aggregation designHistorical return and complaint ratesAverage order value and basket sizePurchase frequency and recencyCustomer lifetime value proxiesLesson 8Promotion and pricing features: effective_unit_price, discount_pct, discount_applied flagBuild promo and price features like real unit price, discount share, and promo flags to show promo strength, margin effects, and customer price feel over time.
Computing effective unit priceDiscount percentage and depthBinary and multi-level promo flagsStacked and overlapping promotionsPrice elasticity proxy featuresLesson 9Train/test split strategies for time-series/order data (time-based split, stratified by target, customer holdout)Plan train/test splits for time-based sales data, using time cuts, target layers, and customer holds to get real, fair performance checks.
Pitfalls of random splits in time dataTime-based and rolling window splitsStratified splits for imbalanced targetsCustomer and store level holdoutsCross-validation for temporal dataLesson 10Geographic and logistics features: country-level metrics, shipping zones, typical delivery_time distributionBuild place and delivery features with country stats, ship areas, and delivery time spreads to catch work limits, area habits, and service changes in predictions.
Country and region level aggregationsDefining shipping zones and lanesDelivery time distribution featuresDistance and cross-border indicatorsService level and SLA featuresLesson 11Standardizing and cleaning categorical variables: product_category, country, marketing_channel, device_typeClean and steady category fields like product type, country, sales channel, and device by fixing labels, joining rare ones, and matching lists across data.
Detecting inconsistent category labelsString normalization and mappingMerging rare and noisy categoriesMaintaining category taxonomiesDocumenting categorical cleaning
