The MET Office has been responsible for monitoring UK Weather since its inception in 1854. 36 stations in the UK gather information that is used to predict future weather patterns and issue public advice. More recently, these large datasets have become useful to investigate how the UK climate has changed over the past 150+ years.
Stream Category: Data Visualisation
Individual crime and anti-social behaviour (ASB) incidents, including street-level location information and subsequent police and court outcomes associated with the crime. The data is provided by the 43 geographic police forces in England and Wales, the British Transport Police, the Police Service of Northern Ireland and the Ministry of Justice.
Stream Category: Data Analysis
Can you predict the daily price variation of electricity futures contracts in England and Scotland based on weather, energy, and commercial data? A multitude of factors influence electricity prices daily, including local weather variations, long-term phenomena such as global warming, geopolitical events, and each country’s specific energy mix. Your challenge is to navigate this complexity and create a model that can accurately predict these price variations.
Stream Category: Machine Learning
Visual Appeal: The overall aesthetic quality and attractiveness of the data visualization, including color schemes, typography, layout, and visual elements.
Clarity and Communication: How effectively the visualization communicates the intended message and insights.
Insightfulness: The depth and quality of the insights derived from the visualization.
Storytelling and Narrative: The ability to tell a compelling story or narrative through visualisation.
Innovation and Creativity: The level of innovation and creativity demonstrated in the visualisation.
Presentation
Data Cleaning and Preprocessing
Data Exploration and Descriptive Statistics: The use of appropriate descriptive statistics, data visualization, and summary measures to gain insights into the dataset.
Data Analysis Techniques: The selection and application of appropriate data analysis techniques competition’s objectives and dataset characteristics.
Interpretation and Insight Generation: The ability to interpret the results of data analysis accurately and extract meaningful insights and actionable recommendations from the findings.
Documentation and Presentation
Creativity and Innovation: The level of creativity and innovation demonstrated in the data analysis approach.
Presentation
Predictive Performance: The accuracy, precision, recall, F1 score, or other relevant metrics that measure the predictive performance of the models.
Generalization and Robustness: The ability of the models to generalize well to unseen data and handle variations, noise, or outliers in the dataset.
Documentation and Code Quality: The clarity, organization, and completeness of the documentation and code accompanying the machine learning models.
Presentation: Clearly explains the models, their performance, their strengths and weaknesses, and their applicability. This could include visual aids such as charts or diagrams, clear and concise explanations, and a logical flow of information.