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German Electricity Load Forecasting Using Time Series and Machine Learning Models
This individual assessment focuses on analysing and forecasting German electricity load using time-series analysis, statistical forecasting models and machine-learning approaches. Students are required to work with publicly available electricity data from the Open Power System Data platform and investigate the characteristics of the German time series before developing and comparing a range of forecasting models. The project combines exploratory data analysis, time-series modelling, regression-based forecasting and neural-network modelling, with particular emphasis on critical evaluation of modelling choices and forecast performance. The first stage requires students to retrieve the 60-minute electricity data and isolate the German data using the country identifier “DE”. The data should be aggregated into weekly and daily values using tools such as Pandas, with observations retained from 1 January 2015 through October 2020. Students must produce initial plots and conduct exploratory data analysis to identify the main components of the time series, including possible trend and seasonal patterns. Time-series analysis should then be performed to investigate and test for non-stationarity. The modelling component begins with benchmark forecasting methods such as Mean, Naive, Seasonal Naive and Drift forecasts. Students are required to use a two-year forecast horizon and compare the performance of these benchmark approaches. An autoregressive modelling approach must then be developed using SARIMA where appropriate. Students must identify suitable model parameters using the AIC likelihood method, considering the required combinations of p, d, q, P, D and Q parameters. Model fit should be assessed through residual analysis, including inspection of residual distributions and autocorrelation plots. Forecasts should include confidence intervals and performance should be evaluated using suitable metrics such as RMSE. The assessment also requires an exogenous temperature variable to be incorporated into a SARIMAX model. Berlin is specified as the representative location for German temperature data. Students must recognise the distinction between a true operational forecast and an explanatory or conditional forecast when future observed temperature values are used. The temperature variable should also be used to produce weekly forecasts. Further modelling requires students to apply a feature-based regression model, such as Random Forest or Gradient Boosting Regression, to the weekly electricity-load and temperature data. The final modelling stage uses hourly data with a Long Short-Term Memory (LSTM) neural network. Students must conduct a literature review relating to LSTM applications for the forecasting task, build and tune the model, evaluate its performance and forecast the final two years of available data. The report must critically compare the models rather than simply present numerical results. Students must address specific questions concerning improvement over the Seasonal Naive benchmark, prevention of data leakage when constructing temperature lag features, SARIMAX parameter choices, the value and availability of temperature and holiday covariates, and the relative interpretability and complexity of SARIMAX, feature-based and neural-network models. Students must also recommend one model for operational use based on accuracy, uncertainty, interpretability and maintenance considerations. The final report should be 6–8 pages and include model forecast plots, an evaluation-metrics comparison table, critical discussion of the results, comparison with real data, future improvements and appropriate references. Students must also create a GitHub repository containing the codebase and follow appropriate coding practices. The submitted code must reproduce the figures, models and numerical values presented in the report.
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Applied Modelling and Visualisation
2,500 words
Applied Modelling and Visualisation – Hexawing Airways Passenger Satisfaction Analysis
This assessment is a 2,500-word consultancy report for the Applied Modelling and Visualisation module within the MSc Management with Data Analytics programme at BPP University. The assignment requires students to work as Data Analytics Consultants for the fictional Hexawing Airways and analyse a passenger satisfaction dataset containing more than 103,000 records from the airline's database. The purpose of the assessment is to apply machine learning, data analysis and visualisation techniques to identify factors that influence passenger satisfaction and communicate meaningful findings to a varied corporate audience, including the Chief Executive Officer, senior flight personnel and cabin crew. The dataset contains a range of passenger and flight-related features, including gender, satisfaction status, age, age band, type of travel, travel class, flight distance, destination, continent and ratings for services such as inflight Wi-Fi, online booking, gate location, food and drink, online boarding, seat comfort, inflight entertainment, onboard service, leg room, baggage handling, check-in service, inflight service and cleanliness. It also includes departure and arrival delay information. These variables provide the basis for exploratory analysis, predictive modelling and visual communication of passenger satisfaction patterns. The assessment requires the development of a data-driven solution using Python and relevant Python libraries. Students must follow an established analytical methodology such as PPDAC or CRISP-DM and demonstrate an Extract, Transform and Load process, data preparation, exploratory data analysis and appropriate visualisations. Two analytical models must be selected, trained and tested to predict passenger satisfaction. The available modelling approaches include Logistic Regression, Naive Bayes, Decision Tree, Bagging, Random Forest, AdaBoost, XGBoost, Artificial Neural Networks or another appropriate state-of-the-art algorithm. The second task requires critical analysis of the two selected models, including their strengths and limitations, an explanation of the chosen loss function, discussion of accuracy metrics and a comparison table of model performance. The third task focuses on communicating findings through data visualisation, including outputs such as correlation matrices, heat maps and confusion matrices. The analysis should explain how exploratory data analysis guided model selection and how visualisation techniques communicate insights effectively. The final report should demonstrate the ability to formulate data-driven solutions, critically evaluate analytical models and appraise data visualisation techniques. The assessment also requires independent research, appropriate academic referencing and supporting evidence from the analytical process. A pre-run Python notebook must be embedded in the MS Word submission or provided through an appropriate shared link.
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assessment requires students to develop a comprehensive analytical solution to a real-world healthcare prediction problem using the WiDS Datathon 2025 Health Outcomes Prediction Dataset. The dataset contains socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents, with the principal objective of developing predictive models for ADHD diagnosis. The assessment is designed to demonstrate the complete data-science lifecycle, from data preparation and exploratory analysis through predictive modelling, interpretation and evidence-based recommendations. Students begin by exploring the dataset's features, data types and distributions before addressing missing values, outliers and other inconsistencies. Appropriate feature engineering should be undertaken where necessary, followed by Exploratory Data Analysis (EDA) using relevant visualisations to identify relationships, patterns and correlations within the data. Students with limited computational resources may use a representative subset, provided that the sampling method preserves the integrity and distribution of the original dataset and is clearly justified. A major component of the assignment involves developing and comparing at least three classification models. Appropriate techniques may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Model performance should be evaluated using measures including accuracy, precision, recall, F1-score and ROC-AUC, allowing students to identify the strongest-performing model through systematic comparison. The assessment also requires model interpretation and explainability. Students should explain the results of the selected model and may apply techniques such as SHAP or LIME to investigate feature importance and individual predictions. A feature-importance visualisation must be produced, and the most influential variables should inform practical recommendations for healthcare professionals regarding the potential use of predictive modelling in supporting earlier ADHD diagnosis and intervention. Overall, the assignment integrates data cleaning, exploratory analytics, predictive modelling, model comparison, explainable AI and research-informed healthcare recommendations. Students must submit a comprehensive report of no more than 2,500 words, alongside a Jupyter Notebook containing the implementation and outputs. The report must use Harvard referencing, with appropriate academic research integrated into the analysis, recommendations and conclusion.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Data Science / Artificial Intelligence and Machine Learning
2,500 words
Predicting ADHD Diagnosis Using Machine Learning and Explainable Data Science
This Data Science assignment focuses on developing a comprehensive analytical solution to a real-world healthcare prediction problem. Using the WiDS Datathon 2025 Health Outcomes Prediction Dataset, students are required to analyse complex and high-dimensional healthcare data containing socio-demographic information, diagnostic variables and functional MRI data relating to children and adolescents. The principal predictive objective is to determine ADHD diagnosis from the available features. Students may use a representative subset of the dataset where computational resources are limited, provided that the sampling approach maintains the integrity and distribution of the original data and is appropriately justified. The assessment requires a complete data-science workflow beginning with data understanding and preprocessing. Students investigate the dataset's features, data types and distributions before addressing missing values, outliers and inconsistencies. Appropriate feature engineering should then be undertaken where it can improve the predictive capability of the models. Exploratory Data Analysis is used to identify important patterns, relationships and correlations, supported by relevant visualisations that communicate meaningful insights. A major component of the work involves the development and comparison of at least three classification models for predicting ADHD diagnosis. Suitable approaches may include Logistic Regression, Random Forest, Gradient Boosting and Neural Networks. Models are evaluated using performance measures including accuracy, precision, recall, F1-score and ROC-AUC, after which the most effective model is selected based on the evidence obtained. The assessment also places substantial emphasis on model interpretation and explainability. Students must interpret the selected model and may use approaches such as SHAP or LIME to explain feature importance and individual predictions. A feature-importance visualisation is required, and the most influential variables should inform practical recommendations. The final section translates analytical findings into recommendations for healthcare professionals, considering how predictive modelling could assist early ADHD diagnosis and intervention. Research literature must be integrated into the recommendations and conclusion. The assessment therefore combines preprocessing, exploratory analysis, predictive modelling, explainable AI and evidence-based healthcare decision-making within a single applied data-science project. The required report is a maximum of 2,500 words, with code, supplementary charts and tables permitted in appendices. A Jupyter Notebook containing the implementation and outputs is also required. Harvard referencing must be used throughout.
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