- Solar flares are intense bursts of radiation which can disrupt the power grids of a continent, shut down the GPS system or irradiate people exposed in space.
- Developing systems for predicting solar flares would allow us to precisely aim our observation instruments at upcoming events, and eventually enable countermeasures against such worst-case scenarios.
- The prediction of solar flares proves to be a challenging problem, some even compare it to weather forecasting. And regarding Machine Learning, I find this dataset to be particularly challenging because of the complexity of a single sample (up to 40 images), the relatively small size of samples (8'000 for training), and the fact that it is a regression problem. Intel OneAPI makes it to run faster than other notebooks.
- The purpose of this study is based on the available data, it was estimated numbers of the solar flares production in specific region on sun in the following 24 hours. Accuracy can change due to parameters.
The Intel® OneAPI toolkit, Intel® OneDAL enables models to be trained. Deliver high-performance computing tools to build, analyze, optimize, and scale AI, machine learning, and deep learning applications.
- Logistic Regression
- Tensorflow
- Scikit learn
- Keras Preprocessing
- Pandas, numpy, seaborn, matplotlib
- scipy.io, nibabel, csv
- PIL, keras.preprocessing
- tensorflow.keras.preprocessing.image
- Scikit learn
✅Building application using intel oneDAL: The Intel oneAPI Data Analytics Library contributes to the acceleration of big data analysis by providing highly optimised algorithmic building blocks for all phases of data analysics (preprocessing, transformation, analysis, modelling, validation and decision making) in batch, online and distributed processing modes of computation. The library optimizes data ingestion along with algorithmic computation to increase throughput and scalability.
✅Understanding of the data: You would have learned how to preprocess and clean the data, as well as how to handle missing values and categorical variables. You may also have conducted exploratory data analysis to gain insights into the relationships between the variables.
✅Selection of appropriate algorithms: You would have learned how to select appropriate machine learning algorithms for the given problem. For Example, Logistic Regression may be useful for binary classification problems, while decision trees may be better suited for multiclass problems.
✅Machine Learning: I likely learned about different machine learning algorithms and how they can be applied to predict cardiovascular disease and make recommendations for patients.
✅Data Analysis: I likely gained experience in collecting and analysing large amounts of data, including historical data, to train our machine learning models.
✅Comparison of model performance: You would have learned how to compare the performance of different models using appropriate statistical tests or visualizations. This can help you choose the best model for the given model.
✅Collaboration: Building a project like this likely required collaboration with the team of experts in various fields, such as medical science, machine learning and data analysis and I likely learned the importance of working together to achieve common goals.
These are just few examples of the knowledge and skills that I likely gained while building this project. Overall, building a Solar Flares Prediction is a challenging and rewarding experience that requires a combination of technical expertise and Universal Knowledge.