Weather-data preparation
The project used historical and real-time weather information in an ETL-style process, organising observations before analysis and model exploration.
University project / Project case study
Weather Forecasting & Data Pipelines
Turning source data into interpretable forecasts
THE WORKFLOW
1Weather inputs
2Cleaning & ETL
3Forecast visualisation
Project overview
Project context & objective
This university project examined weather forecasting as an end-to-end information workflow: ingesting observations, preparing them for analysis, exploring forecasting approaches and presenting outputs to users.
Forecasting involves preparing reliable inputs and communicating the limits of predictive output. This project explored historical and real-time weather data processing.
Weather observations are time-dependent, and reliable forecasts rely on consistent inputs. Data arriving from different periods or sources must be organised so time-series and machine-learning methods can interpret the information correctly.
Methodology
From inputs to insights
The following stages explain the workflow and the technical decisions behind this project.
Statistical forecasting
ARIMA was one of the forecasting approaches explored. It provides a time-series perspective on patterns in historical observations.
Machine-learning exploration
Random Forest and scikit-learn were also part of the project toolkit, representing an alternative supervised modelling approach.
Communicating forecasts
Power BI was used to visualise forecasting outputs. Presenting results is important because an analytical prediction must be understandable to users beyond the developer.
Technology
Tools & methods
PythonARIMARandom Forestscikit-learnPower BI
Deliverables
Project outputs & learning
A forecasting workflow comparing statistical and machine-learning approaches, with results presented through Power BI.
What I learned
Forecast quality depends on reliable inputs, documented assumptions and explainable outputs.
Key deliverables
- Structured weather-data processing and preparation workflow
- Forecasting exploration using ARIMA and machine-learning approaches
- Power BI visualisation of forecast-related outputs
Transferable skills
Skills & practical relevance
Data-flow mapping, validation of input quality, assumptions, output communication and analytical system limitations.
- Data-flow mapping and validation
- Assumptions and limitations of forecasting
- Design of understandable reporting outputs
- Translating analytical results into user-facing information
The materials provided do not establish exact datasets, forecast horizons, evaluation metrics or deployment of a live forecasting service. This is described as university project work.