https://github.com/namratha2301/cognitiveemotion

Identifying Emotions from EEG Activity
https://github.com/namratha2301/cognitiveemotion

data-science gru matplotlib python3 scikit-learn seaborn tensorflow xgboost

Last synced: about 1 month ago
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Identifying Emotions from EEG Activity

• Host: GitHub
• URL: https://github.com/namratha2301/cognitiveemotion
• Owner: Namratha2301
• Created: 2022-10-23T08:51:29.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2024-11-06T16:27:18.000Z (2 months ago)
• Last Synced: 2024-11-06T17:32:54.972Z (2 months ago)
• Topics: data-science, gru, matplotlib, python3, scikit-learn, seaborn, tensorflow, xgboost
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/1kN7CFoHXXKD8FWbcWiLgs3R7y30u4Dma?usp=sharing
• Size: 57.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
Cognitive Emotion


About the Project

The project uses the EEG Brainwave Dataset from Kaggle to create 

machine learning models that allow the user to predict the emotion of a person 

given his EEG Brainwave Data.The dataset was downloaded from Kaggle. 

Here is the  link to the dataset.


Setup


To run the notebook one can either prefer using Google Colab the better method or run the notebook locally. To run using Colab just use the link at the end of the ReadMe file.

For running the notebook locally, follow the steps [Windows]:

1. Clone the repository using `git clone https://github.com/Namratha2301/CognitiveEmotion.git`

2. Set directory to cloned repo `cd CognitiveEmotion`

3. Create a python virtual environment for the project using `python -m venv env`

4. Activate the environment using `env\Scripts\activate`

5. Install the dependencies using `pip install -r requirements.txt`

6. Open the Jupyter Notebook IDE using `jupyter notebook`

7. The Jupyter Notebook IDE should open up allowing you to run the file

Machine Learning Models and Scores


S.No

Model

Package

Score

1

Random Forest Classifier

SciKit-Learn

98.7%

2

Logistic Regression Classifier

SciKit-Learn

93.2%

3

Logistic Regression Classifier With 2 PC

SciKit-Learn

77.5%

4

Logistic Regression Classifier with 10 PC

SciKit-Learn

86.6%

5

Linear Support Vector Machine Classifier (SVM)

SciKit-Learn

96.57%

6

Extreme Gradient Boosting Classifier

XGBoost

99.39%

7

GRU

TensorFlow

95.46%

Link to Colab File

CognitiveEmotionColab