Aspiring Data Analyst focused on building a career in data analysis, with a special interest in cybersecurity trends.
ClimateWins is exploring the use of machine learning to better understand and predict the impacts of climate change across Europe and, eventually, globally. The organization has gathered diverse datasets, including hurricane forecasts from NOAA (USA), typhoon records from JMA (Japan), and global temperature data. Their primary goal is to assess whether advanced machine learning models can accurately categorize and predict weather patterns, especially in mainland Europe, where extreme weather events have become more frequent over the past 10–20 years. With over a century of climate data, ClimateWins aims to build models that not only explain historical patterns but also help forecast future conditions, including potentially hazardous weather.
- How can machine learning be applied to weather prediction?
- Is machine learning effective when working with weather-related data?
- Are there any ethical concerns specific to using AI in this context?
- What are the historical extremes in European temperature records?
- Can machine learning help predict favorable or dangerous weather conditions on a given day?
This data is sourced from the European Climate Assessment & Dataset (ECA&D).
The project uses weather data from 18 weather stations across Europe, covering the period from the late 1800s to 2022. The dataset includes daily observations of variables such as: -Temperature -Wind speed -Snowfall -Global radiation -And other meteorological indicators
Python – for data analysis, model building, and evaluation PowerPoint – for presenting findings and visualizations
Check out my Jupyter notebooks scripts in the attached folder 'Scripts', that demonstrate my data wrangling, analysis, and visualization skills.
youtube presentation link : https://www.youtube.com/watch?v=m0KEIutMzU4&t=1s