Lesson 1Feature scaling and transformation: log transforms for skewed revenue/quantity, robust scalingUse scaling and changes to steady variation and cut skewness in revenue and quantity, with log transforms, tough scaling, and power changes 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 weekdays, hours, seasons, recency, and tenure, keeping time order to avoid leaks in predictions.
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 strategies for numbers and categories, like median, KNN, model-based, mode, and 'unknown' tags, checking for bias, spread, and strength in the filled data.
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)Build target variables for main predictions, like return flags, revenue amounts, and late delivery tags, with clear meanings matched to checks.
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 featuresTry encoding for categories, from one-hot to target, frequency, and embeddings, avoiding leaks, adding controls, and managing many-option features.
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 outliers in price, quantity, delivery days, and revenue using stats and business sense, keeping data while guarding models.
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 values, buy frequency, and time since last buy to catch lifetime patterns and boost 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 percent, and flags to show promo strength, margin effects, and price sensitivity 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 transaction data, using time splits, target layers, and customer holds for 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 distributionCreate location and logistics features with country stats, shipping areas, and delivery time spreads to catch ops limits, regional habits, and service changes.
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 standardise categories like product types, countries, marketing channels, and devices by fixing labels, grouping rares, and matching taxonomies.
Detecting inconsistent category labelsString normalization and mappingMerging rare and noisy categoriesMaintaining category taxonomiesDocumenting categorical cleaning
