5 Machine Learning Projects to Celebrate Earth Month as a Developer | by Behic Guven | Apr, 2023

In-depth overview of environmentally friendly projects

It’s hard to overstate just how much data there is in the world today. We’re drowning in data from the clickstreams generated by billions of internet users to the terabytes of data produced by industrial equipment and scientific experiments. Data is becoming more and more like an exchange currency of the age of internet.

Data also represents a tremendous opportunity; by analyzing and understanding data, we can gain previously impossible insights and use those insights to solve some of humanity’s most pressing problems.

Whether it’s developing machine learning models to predict natural disasters or building data-driven tools to optimize energy usage, there are countless opportunities where we can use our data science knowledge to promote sustainability and protect our environment.

The potential of data science is truly awe-inspiring, and it’s hard not to be excited about its possibilities and outcomes. By using advanced analytics and machine learning to extract insights from massive datasets, we can make a real difference in creating a more sustainable and equitable future for ourselves and next generations. It’s an exciting times we are living in, and I can’t wait to see what the future holds.

In this article, I will share 5 hands-on data science/ machine learning projects that will give us some insights on how we can use technology to solve real-world problems. I thought this will be a nice way to celebrate Earth month and challenge ourselves and work on these projects. I tried to keep the list diverse both project topic wise and programming expertise wise. Great projects to have in a portfolio as well.

Let’s get started!

Here is the list of projects we will talk in this article:

1. Bear Conservation Project
2. LANL — Earthquake Prediction
3. EDSA — Climate Change Belief Analysis
4. Solar Power Forecasting with ML Techniques
5. Open Footprint
6. Conclusion

Bear Conservation with ML, Serverless and Citizen Science

Let’s start with cute and huge creatures, bears.

Bear conservation project addresses the challenge of monitoring and tracking bear populations in remote areas. This is important for conservation efforts, as it can help researchers understand the behavior and distribution of bear populations and identify potential threats to their survival.

The project combines several machine-learning techniques with computer vision technology to analyze citizen science data collected by volunteers. Specifically, it uses object detection and face recognition algorithms to identify the bears in images and videos. These algorithms are trained using labeled data, like ImageNet to recognize specific features of bears, such as their shape, size, and color.

Once the bear images and videos are processed, the data is stored in an Amazon S3 (Simple Storage Service) bucket and analyzed using Amazon SageMaker. This enables researchers and conservationists to run deeper analysis on the data, including identifying patterns and trends in bear behavior and population distribution.

The project also incorporates a serverless architecture, by using AWS Lambda and Amazon DynamoDB services to process and store data. The serverless computing approach allows the system to automatically scale up or down in response to changes in demand, reducing costs and improving efficiency.

Overall, the project demonstrates the power of machine learning and serverless computing technologies to address complex environmental challenges and promote conservation efforts. Great use case example of AWS (Amazon Web Services) to make the world a better place.

Learn more about the project: BearResearch.org.

Video Demonstration by Ed Miller.

Can we predict upcoming earthquakes using machine learning?

Earlier this year, on Feb 6th, a magnitude 7.8 earthquake occurred in southern Turkey near the northern border of Syria. This quake was followed approximately nine hours later by another magnitude 7.5 earthquake. According to UN’s Press Briefing on March 23rd, the death toll has been climbing and has already passed 56000 people. Our hearts goes with the affected ones, and wishing them patience and recovery.

Maybe it’s hard to predict the exact timings of earthquake disasters, but with the help of advanced technologies that we already have today, I believe that we can minimize the impact of the destruction, so that less lives get affected.

This recent event was the main reason I wanted to include an earthquake prediction project in this list.

The Los Alamos National Laboratory (LANL) Earthquake Prediction project is simply a research effort to develop machine learning models to predict earthquakes. The project is based on the premise that earthquakes are typically preceded by detectable changes in the Earth’s crust, such as variations in seismic activity or other geophysical variables.

The LANL Earthquake Prediction project has been ongoing for several years and has involved the collaboration of researchers from multiple disciplines, including seismology, geology, and computer science.

This Earthquake prediction project is motivated by the fact that earthquakes can have devastating consequences, causing widespread damage to infrastructure and loss of life. If earthquakes could be predicted with greater accuracy, it could enable communities to take proactive measures to mitigate their impact.

The project has focused on developing and testing various machine learning models, including neural networks, decision trees, and support vector machines. These models were trained using large datasets of seismic data, including seismographs, GPS sensors, and satellite imagery, along with other geophysical variables such as ground motion, strain, and tilt.

The project has also involved in the development and creation of software tools for visualizing and interpreting the results. The end goal was to identify patterns and correlations that can be used to make more accurate earthquake predictions.

Despite significant progress, many challenges must be overcome before making accurate and reliable earthquake prediction can be achieved. Here are some of the challenges that was faced in this project:

One of the biggest challenges is the sheer complexity of the Earth’s crust, which can be influenced by many factors, including tectonic activity, volcanic eruptions, and human activities such as mining and fracking. Additionally, earthquakes are inherently unpredictable, and there is always a degree of uncertainty associated with any prediction.

Despite these challenges, the LANL Earthquake Prediction project represents a significant step forward in developing machine learning models for predicting natural disasters. These models could ultimately save countless lives and reduce the impact of earthquakes on communities worldwide.

Learn more about the project: LANL Earthquake Prediction.

Dataset is publicly available on Kaggle.

Can we predict an individual’s belief in climate change based on their historical tweet data?

This project was a data science project aimed at analyzing public attitudes toward climate change in South Africa using machine learning techniques.

The project was undertaken by the Explore Data Science Academy (EDSA), an organization in South Africa that provides training in data science and related fields. The project was part of their Data Science for Social Impact program, which aims to apply data science techniques to address social and environmental issues. Their program was also supported by AWS.

The project involved collecting data through an online survey distributed to a representative sample of South African residents. The survey contained open-ended and closed-ended questions to capture respondents’ beliefs about climate change, demographic information, and other relevant factors.

Once the data was collected, the project team cleaned and processed it using various data-wrangling techniques to ensure it was suitable for analysis. They then employed a range of algorithms to explore the data and draw insights about public attitudes towards climate change.

One essential technique the team used was natural language processing (NLP), which involves using computational methods to analyze human language. The team applied NLP to the open-ended responses in the survey to identify common themes and sentiments among respondents. This allowed them to better understand the factors influencing public attitudes toward climate change in South Africa.

The team also applied classification algorithms to predict respondents’ beliefs about climate change based on their demographic information and other factors. This involved training machine learning models to classify respondents as either “believers” or “non-believers” in climate change. They also created visualizations to help communicate their findings to a broader audience. This included creating interactive dashboards and data visualizations.

Overall, this project was an important initiative that used data science techniques to gain insights into public attitudes towards climate change in South Africa. The data is publicly available, feel free to challenge yourself and apply your data science skills.

Learn more about the project: EDSA — Climate Change.

Learn how they use AWS at Explore Data Science Academy: Video.

Dataset is publicly available on Kaggle.

A Research Paper by EMIL ISAKSSON and MIKAEL KARPE CONDE.

The paper focuses on the problem of forecasting solar power generation, which is essential for integrating and managing solar energy in the grid. Accurate solar power forecasting can help grid operators make informed decisions related to grid management and reduce the cost of balancing supply and demand in the electricity market.

The authors used a dataset of historical weather and solar power generation data of the aggregated daily energy output of the solar radiation, measured over eight years. This dataset was preprocessed by applying feature extraction and normalization techniques to prepare it for use with machine learning algorithms.

Several machine learning models were trained and evaluated using the dataset, including Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Networks (ANN). These models are evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

Quick insight: The study finds that the ANN model outperforms the other models’ accuracy and robustness. The ANN model can capture complex nonlinear relationships between the input features and the solar power generation output, making it a more effective forecasting tool.

The paper also discusses the potential practical applications of the developed forecasting model, such as aiding grid operators in making decisions related to grid management and reducing the cost of balancing supply and demand in the electricity market.

The study concludes that machine learning techniques, specifically the ANN model, can be practical tools for accurate solar power forecasting, ultimately contributing to integrating and managing solar energy in the grid.

Learn more about the project: Solar Power Forecasting.

Environmental Profiles for Individuals, Organizations, Products and Communities

Climate change is one of the most pressing issues facing our planet today. The increased carbon dioxide and other greenhouse gases in the atmosphere have resulted in rising temperatures, melting glaciers, and more frequent extreme weather events. As individuals, organizations, and communities, we all have a role to play in reducing our carbon footprint and mitigating the impact of climate change.

To address this issue, a team of developers and environmentalists have created Open Footprint — an open-source carbon footprint project to provide environmental profiles for individuals, organizations, products, and communities.

The project was designed to be modular and customizable, allowing users to select the metrics and calculations most relevant to their needs. It is hosted on GitHub, and the code is available for anyone to download, use, and contribute.

The project includes various features, such as data visualization and reporting tools, that make it easier for users to understand and communicate their carbon footprint data. The project is licensed under the GNU General Public License, ensuring the software remains open-source and freely available to everyone.

Overall, the Open Footprint project is an important initiative that promotes transparency and accountability around carbon footprints and other environmental impact metrics. By providing an open and accessible platform for measuring and tracking ecological data, the project aims to empower individuals, organizations, and communities to make more informed decisions about their environmental impact and take action to reduce their carbon footprint.

The goal of the project is to create a free, open, and accessible platform for measuring and tracking carbon footprints, as well as other environmental impact metrics. The project’s modular and customizable design also ensures that it is flexible enough to meet the needs of a wide range of users, making it a valuable tool for anyone interested in reducing their environmental impact.

Learn more about the project: Open Footprint.

Example use case: EPA’s Carbon Footprint Calculator.

In conclusion, as today’s developers and data scientists, we have the power to make a real difference. Whether it’s predicting natural disasters or optimizing energy usage, data science, and machine learning offer us powerful tools that we can use to create a better, more sustainable future for ourselves and the next generations.

So let’s roll up our sleeves and get to work!

Working on hands-on programming projects is the best way to sharpen our skills. Feel free to reach me or respond to this article if you have any questions or feedback about the projects.

I am Behic Guven, and I love sharing stories on programming, technology, and education. Consider becoming a Medium member if you enjoy reading stories like this and want to support my journey. Ty,

If you are wondering what kind of articles I write, here are some:

In-depth overview of environmentally friendly projects

It’s hard to overstate just how much data there is in the world today. We’re drowning in data from the clickstreams generated by billions of internet users to the terabytes of data produced by industrial equipment and scientific experiments. Data is becoming more and more like an exchange currency of the age of internet.

Data also represents a tremendous opportunity; by analyzing and understanding data, we can gain previously impossible insights and use those insights to solve some of humanity’s most pressing problems.

Whether it’s developing machine learning models to predict natural disasters or building data-driven tools to optimize energy usage, there are countless opportunities where we can use our data science knowledge to promote sustainability and protect our environment.

The potential of data science is truly awe-inspiring, and it’s hard not to be excited about its possibilities and outcomes. By using advanced analytics and machine learning to extract insights from massive datasets, we can make a real difference in creating a more sustainable and equitable future for ourselves and next generations. It’s an exciting times we are living in, and I can’t wait to see what the future holds.

In this article, I will share 5 hands-on data science/ machine learning projects that will give us some insights on how we can use technology to solve real-world problems. I thought this will be a nice way to celebrate Earth month and challenge ourselves and work on these projects. I tried to keep the list diverse both project topic wise and programming expertise wise. Great projects to have in a portfolio as well.

Let’s get started!

Here is the list of projects we will talk in this article:

1. Bear Conservation Project
2. LANL — Earthquake Prediction
3. EDSA — Climate Change Belief Analysis
4. Solar Power Forecasting with ML Techniques
5. Open Footprint
6. Conclusion

Bear Conservation with ML, Serverless and Citizen Science

Let’s start with cute and huge creatures, bears.

Bear conservation project addresses the challenge of monitoring and tracking bear populations in remote areas. This is important for conservation efforts, as it can help researchers understand the behavior and distribution of bear populations and identify potential threats to their survival.

The project combines several machine-learning techniques with computer vision technology to analyze citizen science data collected by volunteers. Specifically, it uses object detection and face recognition algorithms to identify the bears in images and videos. These algorithms are trained using labeled data, like ImageNet to recognize specific features of bears, such as their shape, size, and color.

Once the bear images and videos are processed, the data is stored in an Amazon S3 (Simple Storage Service) bucket and analyzed using Amazon SageMaker. This enables researchers and conservationists to run deeper analysis on the data, including identifying patterns and trends in bear behavior and population distribution.

The project also incorporates a serverless architecture, by using AWS Lambda and Amazon DynamoDB services to process and store data. The serverless computing approach allows the system to automatically scale up or down in response to changes in demand, reducing costs and improving efficiency.

Overall, the project demonstrates the power of machine learning and serverless computing technologies to address complex environmental challenges and promote conservation efforts. Great use case example of AWS (Amazon Web Services) to make the world a better place.

Learn more about the project: BearResearch.org.

Video Demonstration by Ed Miller.

Can we predict upcoming earthquakes using machine learning?

Earlier this year, on Feb 6th, a magnitude 7.8 earthquake occurred in southern Turkey near the northern border of Syria. This quake was followed approximately nine hours later by another magnitude 7.5 earthquake. According to UN’s Press Briefing on March 23rd, the death toll has been climbing and has already passed 56000 people. Our hearts goes with the affected ones, and wishing them patience and recovery.

Maybe it’s hard to predict the exact timings of earthquake disasters, but with the help of advanced technologies that we already have today, I believe that we can minimize the impact of the destruction, so that less lives get affected.

This recent event was the main reason I wanted to include an earthquake prediction project in this list.

The Los Alamos National Laboratory (LANL) Earthquake Prediction project is simply a research effort to develop machine learning models to predict earthquakes. The project is based on the premise that earthquakes are typically preceded by detectable changes in the Earth’s crust, such as variations in seismic activity or other geophysical variables.

The LANL Earthquake Prediction project has been ongoing for several years and has involved the collaboration of researchers from multiple disciplines, including seismology, geology, and computer science.

This Earthquake prediction project is motivated by the fact that earthquakes can have devastating consequences, causing widespread damage to infrastructure and loss of life. If earthquakes could be predicted with greater accuracy, it could enable communities to take proactive measures to mitigate their impact.

The project has focused on developing and testing various machine learning models, including neural networks, decision trees, and support vector machines. These models were trained using large datasets of seismic data, including seismographs, GPS sensors, and satellite imagery, along with other geophysical variables such as ground motion, strain, and tilt.

The project has also involved in the development and creation of software tools for visualizing and interpreting the results. The end goal was to identify patterns and correlations that can be used to make more accurate earthquake predictions.

Despite significant progress, many challenges must be overcome before making accurate and reliable earthquake prediction can be achieved. Here are some of the challenges that was faced in this project:

One of the biggest challenges is the sheer complexity of the Earth’s crust, which can be influenced by many factors, including tectonic activity, volcanic eruptions, and human activities such as mining and fracking. Additionally, earthquakes are inherently unpredictable, and there is always a degree of uncertainty associated with any prediction.

Despite these challenges, the LANL Earthquake Prediction project represents a significant step forward in developing machine learning models for predicting natural disasters. These models could ultimately save countless lives and reduce the impact of earthquakes on communities worldwide.

Learn more about the project: LANL Earthquake Prediction.

Dataset is publicly available on Kaggle.

Can we predict an individual’s belief in climate change based on their historical tweet data?

This project was a data science project aimed at analyzing public attitudes toward climate change in South Africa using machine learning techniques.

The project was undertaken by the Explore Data Science Academy (EDSA), an organization in South Africa that provides training in data science and related fields. The project was part of their Data Science for Social Impact program, which aims to apply data science techniques to address social and environmental issues. Their program was also supported by AWS.

The project involved collecting data through an online survey distributed to a representative sample of South African residents. The survey contained open-ended and closed-ended questions to capture respondents’ beliefs about climate change, demographic information, and other relevant factors.

Once the data was collected, the project team cleaned and processed it using various data-wrangling techniques to ensure it was suitable for analysis. They then employed a range of algorithms to explore the data and draw insights about public attitudes towards climate change.

One essential technique the team used was natural language processing (NLP), which involves using computational methods to analyze human language. The team applied NLP to the open-ended responses in the survey to identify common themes and sentiments among respondents. This allowed them to better understand the factors influencing public attitudes toward climate change in South Africa.

The team also applied classification algorithms to predict respondents’ beliefs about climate change based on their demographic information and other factors. This involved training machine learning models to classify respondents as either “believers” or “non-believers” in climate change. They also created visualizations to help communicate their findings to a broader audience. This included creating interactive dashboards and data visualizations.

Overall, this project was an important initiative that used data science techniques to gain insights into public attitudes towards climate change in South Africa. The data is publicly available, feel free to challenge yourself and apply your data science skills.

Learn more about the project: EDSA — Climate Change.

Learn how they use AWS at Explore Data Science Academy: Video.

Dataset is publicly available on Kaggle.

A Research Paper by EMIL ISAKSSON and MIKAEL KARPE CONDE.

The paper focuses on the problem of forecasting solar power generation, which is essential for integrating and managing solar energy in the grid. Accurate solar power forecasting can help grid operators make informed decisions related to grid management and reduce the cost of balancing supply and demand in the electricity market.

The authors used a dataset of historical weather and solar power generation data of the aggregated daily energy output of the solar radiation, measured over eight years. This dataset was preprocessed by applying feature extraction and normalization techniques to prepare it for use with machine learning algorithms.

Several machine learning models were trained and evaluated using the dataset, including Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Networks (ANN). These models are evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

Quick insight: The study finds that the ANN model outperforms the other models’ accuracy and robustness. The ANN model can capture complex nonlinear relationships between the input features and the solar power generation output, making it a more effective forecasting tool.

The paper also discusses the potential practical applications of the developed forecasting model, such as aiding grid operators in making decisions related to grid management and reducing the cost of balancing supply and demand in the electricity market.

The study concludes that machine learning techniques, specifically the ANN model, can be practical tools for accurate solar power forecasting, ultimately contributing to integrating and managing solar energy in the grid.

Learn more about the project: Solar Power Forecasting.

Environmental Profiles for Individuals, Organizations, Products and Communities

Climate change is one of the most pressing issues facing our planet today. The increased carbon dioxide and other greenhouse gases in the atmosphere have resulted in rising temperatures, melting glaciers, and more frequent extreme weather events. As individuals, organizations, and communities, we all have a role to play in reducing our carbon footprint and mitigating the impact of climate change.

To address this issue, a team of developers and environmentalists have created Open Footprint — an open-source carbon footprint project to provide environmental profiles for individuals, organizations, products, and communities.

The project was designed to be modular and customizable, allowing users to select the metrics and calculations most relevant to their needs. It is hosted on GitHub, and the code is available for anyone to download, use, and contribute.

The project includes various features, such as data visualization and reporting tools, that make it easier for users to understand and communicate their carbon footprint data. The project is licensed under the GNU General Public License, ensuring the software remains open-source and freely available to everyone.

Overall, the Open Footprint project is an important initiative that promotes transparency and accountability around carbon footprints and other environmental impact metrics. By providing an open and accessible platform for measuring and tracking ecological data, the project aims to empower individuals, organizations, and communities to make more informed decisions about their environmental impact and take action to reduce their carbon footprint.

The goal of the project is to create a free, open, and accessible platform for measuring and tracking carbon footprints, as well as other environmental impact metrics. The project’s modular and customizable design also ensures that it is flexible enough to meet the needs of a wide range of users, making it a valuable tool for anyone interested in reducing their environmental impact.

Learn more about the project: Open Footprint.

Example use case: EPA’s Carbon Footprint Calculator.

In conclusion, as today’s developers and data scientists, we have the power to make a real difference. Whether it’s predicting natural disasters or optimizing energy usage, data science, and machine learning offer us powerful tools that we can use to create a better, more sustainable future for ourselves and the next generations.

So let’s roll up our sleeves and get to work!

Working on hands-on programming projects is the best way to sharpen our skills. Feel free to reach me or respond to this article if you have any questions or feedback about the projects.

I am Behic Guven, and I love sharing stories on programming, technology, and education. Consider becoming a Medium member if you enjoy reading stories like this and want to support my journey. Ty,

If you are wondering what kind of articles I write, here are some: