Lesson 1Deployment considerations: retraining schedule, data flows, watching for changes and testing predictionsPlan putting revenue prediction systems into use, including how often to retrain, automatic data flows, watching for data and idea changes, and ongoing testing to keep good performance in shifting Zambian market conditions.
Building strong data flowsSetting retraining and update timesWatching data and idea changesContinuous testing and comparingAlert and rollback plansLesson 2Sharing forecasts with stakeholders: showing point forecasts and ranges, scenario checks, clear assumptionsShare revenue forecasts well with stakeholders using simple visuals of point and range predictions, scenario checks, and open notes on assumptions, limits, and model risks for Zambian banking.
Showing point and range forecastsScenario and what-if check designsExplaining drivers and main featuresNoting assumptions and limitsFitting messages to stakeholdersLesson 3Machine learning time series models: random forests/gradient boosting with past features, XGBoost/LightGBM, and sequence models (LSTM/GRU)Train machine learning models for time series revenue prediction, including tree groups with past features and sequence models like LSTM and GRU, while dealing with non-steady patterns, seasons, and product differences in Zambia.
Random forests with past featuresGradient boosting, XGBoost, LightGBMGlobal vs local prediction modelsSequence models with LSTM and GRUDealing with non-steady patterns and scalingLesson 4Setting forecasting goals and time frames (e.g., next 3, 6, 12 months)Set forecasting aims for main bank products by picking prediction targets, time frames, and detail levels, and match them with business choices like budgeting, pricing, liquidity plans, and rules or risk reports in Zambia.
Picking revenue targets and unitsChoosing forecast time frames and frequencyMatching forecasts with business choicesDetail by product, group, and areaHandling new products and short pastsLesson 5Finding and getting time series data (public financial series, payment amounts, made-up creation ways)Learn to find, check, and get time series data for bank revenue prediction, including inside product measures, public financial series, and made-up data that safely adds to scarce or noisy past records in Zambian contexts.
Listing inside product revenue seriesUsing public big and market data sourcesGathering payment and deal amount dataChecking data quality, gaps, and changesMade-up data creation for stress casesLesson 6Training and fine-tuning: grid/random search, Bayesian improvement, time-based scoringImprove model work using planned fine-tuning ways, including grid and random search, Bayesian improvement, and time-based scoring that follows time order and focuses on key business time frames in Zambia.
Setting search areas and starting pointsGrid and random search balancesBayesian improvement stepsTime-based checking and scoringEarly stop and resource limitsLesson 7Model grouping and matching: simple model averaging, weighted groups, stacking for time seriesMix many forecasting models for bank revenue using simple averages, weighted groups, and stacking, and use group matching to ensure steady forecasts across products, branches, and levels in Zambian banks.
Simple and weighted model averagingStacking and meta-learners for seriesVariety and links among modelsGroup and bundled matchingChecking group steadiness over timeLesson 8Basic time series methods: ARIMA, ETS, simple and seasonal simple models, breaking down (trend/season)Look at basic time series models for bank revenue, including simple, seasonal simple, ARIMA, ETS, and breakdown, to set reference work and understand trend and season before using complex machine learning in Zambia.
Simple and seasonal simple benchmarksClassic breakdown of trend and seasonARIMA modeling for bank revenue seriesSmoothing growth and ETS typesComparing basics across productsLesson 9Feature building for revenue: pasts, rolling averages/std, differencing, calendar effects, holiday signs, group effects, marketing/campaign flagsBuild predictive features for bank revenue, including pasts, rolling stats, differencing, calendar and holiday effects, group and life cycle signs, and marketing or campaign flags that catch demand shifts and breaks in Zambia.
Past and lead features for revenueRolling averages, changes, and ratiosCalendar, holiday, and pay day effectsGroup and life cycle based featuresMarketing and campaign effect flagsLesson 10Data splitting and cross-checking for time series: train/check/test splits, growing window CV, blocked CVDesign time-aware data splits and cross-checking plans for revenue prediction, including rolling and blocked ways, to avoid leaks, copy real use, and get reliable model work estimates over time in Zambian finance.
Holdout train, check, and test splitsRolling and growing window checkingBlocked cross-checking for seasonsStopping time leaks in featuresTesting over many forecast startsLesson 11Check measures and error study: MAE, RMSE, MAPE, even MAPE, prediction ranges and coverCheck revenue forecasts using measures like MAE, RMSE, MAPE, even MAPE, and range cover, and do detailed error study by group, time frame, and state to find biases and model weak points in Zambia.
Scale-based error measuresPercentage and relative error measuresPrediction ranges and coverTime frame and group level checksState and event-based error studyLesson 12Big and outside add-ons: using CPI, jobless rates, interest rates, movement, Google Trends; feature picking and past matchingAdd big economy and outside add-ons to revenue models, like CPI, jobless, interest rates, movement, and search trends, and learn ways for past matching, scaling, and feature picking to avoid overfit in Zambian data.
Picking key big indicatorsMatching pasts between big and revenueChanging and scaling outside dataFeature picking and steadyingStress and scenario adds with bigs
