Ecopredict Empowering the Circular Economic ideas through AI. The Circular Economic Conundrum: From Data to Decisions It addresses this by evaluating and surfacing the most promising solutions, aiding in the transition to a sustainable, circular economy.It harnesses the power of AI to analyze and assess sustainability initiatives. Built on a BERT-based machine learning framework, it effectively processes environmental text data, categorizing solutions and estimating their potential impact. EcoPredict is a valuable asset for decision-makers in the circular economy, offering data-driven insights to prioritize and quickly identify promising ideas.
Ensure the following libraries are installed in your Jupyter environment:
- TensorFlow
- Hugging Face Transformers
- Scikit-learn
- Pandas
You can install these directly in a notebook cell:
!pip install tensorflow transformers scikit-learn pandasDataset Place your CSV dataset in an accessible directory. Update the file_path in the notebook to point to your dataset file.
- Running the Notebook
- Dataset Loading: Set
file_pathto the location of your dataset. - Preprocessing: The script processes and encodes the text data.
- Data Splitting: Divides the data into training and testing sets.
- Tokenization: Uses the BERT tokenizer for data processing.
- TensorFlow Dataset Conversion: Converts data for TensorFlow compatibility.
- BERT Model Setup: Includes loading, compiling, training, and evaluating the model.
- Dataset Loading: Set
Execution Run each cell in the Jupyter Notebook sequentially after setting up the dataset and installing the dependencies.
Developed by Bahar and Vaibhav