Climate-Change

Introduction

In today’s rapidly changing world, environmental sustainability and addressing climate change have become critical concerns for individuals and businesses alike. Companies are increasingly focusing on reducing their carbon footprint and providing environmentally friendly products and services. To better understand public perception and gauge the acceptance of their sustainable offerings, it is essential for businesses to explore how people perceive climate change and whether they believe it is a real threat.

Objective

In this classification machine learning project, our objective is to create a robust model capable of classifying individuals’ beliefs in climate change based on their novel tweet data. By accurately predicting whether a person believes in climate change or not, we can provide valuable insights to companies engaged in market research, enabling them to understand the sentiments surrounding climate change across diverse demographics and geographic regions. This, in turn, empowers businesses to make informed decisions and develop effective marketing strategies aligned with their target audience’s beliefs and values.

My Role

As a data scientist, I played a significant role in this project. My contributions include:

• Preprocessing the data for a smoother analysis
• Performing an extensive exploratory data analysis (EDA) to gain insights into the tweet dataset.
• Developing various models by training, validating and testing their performance, and selected the best one for deployment.
• Utilizing Comet for version control, ensuring smooth collaboration and tracking model performance.
• Facilitating seamless team collaboration through GitHub, promoting effective version management and code sharing.
• Building the classification dashboard using Strealit App
• Deploying the Streamlit app on AWS EC2, enabling the team to present the project’s results effectively to stakeholders.

Project Demo

Here is a video demonstration showcasing our project in action.

Conclusion

Our team of diverse data science professionals embraced the challenge of a sentiment analysis project focused on climate change. Leveraging advanced classification machine learning algorithms, we analyzed tweet messages, categorizing them to differentiate between those supporting climate change belief and those that did not. Through the step-by-step process of data collection, preprocessing, feature extraction, model selection, training, and evaluation, we worked together to develop a high-performing classification model. By achieving this goal, we contributed to the advancement of climate change research and supported businesses in their endeavors to create a more sustainable future.