The team adopted a Three-Pronged approach for Data Preparation - Explicit Rules, K-Means Clustering and Random Forest, and combined the outputs to identify the common stocks flagged for anomalies across the selected period by all 3 models. They also developed a Tableau dashboard to provide a comprehensive analysis of each stock’s performance in terms of both selected market indicators and financial ratios.
The team utilised Machine Learning models like LongShort-Term Memory (LSTM) and ARIMA GARCH to predict stock price movement and built a Dashboard in PowerBI to present the analysis models, and to help with visualisation of the predictions.
The team aimed to uncover the most influential factors (predictors) that drive box office performance in order to develop a predictive model to estimate box office for upcoming films, factoring in historical data and other relevant characteristics.
The team aimed to develop prediction models that give deeper insights to trends, forecast revenue through movie attributes and aids in optimising session scheduling. Their main approach involved identifying key factors affecting movie attendance, forecasting box office revenue and admissions using machine learning, and optimizing scheduling with goal
programming and data visualization.
The team developed a Box Office Prediction Dashboard and a Session Optimisation Dashboard to help Cathay Cineplexes predict the performance of a prospective movie, and subsequently optimize the programming for the movie.
The team developed a model to automate the typically manual portfolio construction process and efficiently allocate new assets to existing portfolios.
The team aimed to identify the models that generated the highest market returns depending on the market state and established the following recommendations:
Stable Market: Sharpe Base
Sharp Market Decline: Sortino Base
Market Recovery: HMM SortinoMVO
The team developed a predictive model to establish reorder points and alerts, as well as a Dashboard that highlights current and forecasted product demand, aiding DrinkAid in planning future reorder quantities for each component and strategic decisions.
DrinkAid's manual monitoring of financial metrics has resulted in delayed detection of critical Issues. The team had the following objectives:
- Automated data processing for future updates
- Automated dashboarding
- Anomaly detection
- Forecasting of Sales, and Return on Advertising Spending (ROAS)
The team developed a centralized decision-making dashboard that identifies top-selling products, tracks sales by country and platform, and enhances decision-making with accurate, transparent financial data consolidation.
