Challenge background
Virtual currencies are financial assets and exchange currencies, making accurate price prediction essential to understanding the patterns of cryptocurrency behaviours. The cryptocurrency market has become increasingly volatile and unpredictable in recent years, leading many traders and investors to seek out new tools and strategies for predicting price movements and identifying profitable opportunities. Machine learning models offer a powerful solution to this challenge, leveraging vast amounts of historical data and sophisticated algorithms to analyze trends, identify patterns, and generate accurate predictions of future market behavior. By using these models to analyze cryptocurrency market data, traders and investors can gain valuable insights into the underlying dynamics of the market, and make more informed and profitable decisions about when to buy, sell, or hold their assets.
The problem
Traditionally, measuring and monitoring industrial carbon emissions has required manual data collection and reliance on statistical models to predict emissions. This process is often time-consuming, expensive, and prone to errors since it’s difficult to collect large amounts of data accurately and consistently over time. Furthermore, statistical models can’t account for the complexity of industrial processes and may fail to detect emissions due to variations in activity levels, equipment performance, and atmospheric conditions.
However, by leveraging artificial intelligence (AI) to detect and monitor industrial carbon emissions, we can automate this process for greater efficiency, accuracy, and consistency over time. Machine learning algorithms can be trained on large amounts of historical data to identify patterns and anomalies. This allows companies and regulators to proactively detect issues and implement corrective actions before they escalate. By using AI for this purpose, we have the potential to significantly enhance our ability to reduce greenhouse gas emissions while promoting sustainable development.
Machine learning can also help companies monitor their energy consumption better while developing more efficient energy systems through data-driven decision-making. Therefore, using machine learning for measuring industrial carbon emissions has great potential in mitigating negative impacts of climate change while ensuring sustainable economic growth. Decision-makers can develop effective strategies for reducing emissions by identifying high carbon intensity areas while predicting future emissions based on current trends and patterns.
Goal of the project
In this project, the Omdena Riyadh, Saudi Arabia Chapter will develop a deep learning model that predicts CO2 emission values from open-source global emission data. The ultimate goal is to understand contributing variables and create a recommendation system for decision-makers on how to forecast CO2 emissions and reduce environmental risks.
This 8-week project aims to:
- Data Collection and Exploratory Data Analysis.
- Preprocessing.
- Feature Extraction & PCA.
- Development & training of models that will be compared using evaluation metrics.
- Evaluate Baseline & Experimental Models.
- Publish a research presentation.
Project timeline
- 1
Week 1
Establishing teams for data processing, exploratory data analysis, research and presentation teams, deployment team and machine learning modelling teams, and assigning project managers.
- 2
Week 2
Data processing and EDA, start research paper and presentation
- 3
Week 3
ML Models (i.e. SVM, RF, LSTM, CNN, time-series algorithms)
- 4
Week 4
Model evaluation, deployment, research and presentation
What you'll learn
Understanding cryptocurrencies; applying traditional and state of the art ML Models to historical crytocurrency data to make quantifiable predictions.