https://github.com/madhurragarwal/human-activity-recognition
The first project predicts human activities like walking and sitting using the UCI HAR Dataset, while the second applies ML and deep learning models with blockchain integration to log metrics securely.
https://github.com/madhurragarwal/human-activity-recognition
activity-recognition deep-learning machine-learning python tensorflow
Last synced: about 1 month ago
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The first project predicts human activities like walking and sitting using the UCI HAR Dataset, while the second applies ML and deep learning models with blockchain integration to log metrics securely.
- Host: GitHub
- URL: https://github.com/madhurragarwal/human-activity-recognition
- Owner: madhurragarwal
- Created: 2025-02-03T18:29:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-03T18:36:49.000Z (over 1 year ago)
- Last Synced: 2025-04-05T20:09:46.719Z (about 1 year ago)
- Topics: activity-recognition, deep-learning, machine-learning, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 368 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Human Activity Recognition & Model Evaluation with Blockchain
This repository contains two projects:
1. **Human Activity Recognition with Smartphones**
2. **Machine Learning Model Evaluation with Blockchain Integration**
---
## Project 1: Human Activity Recognition with Smartphones
### Overview
This project builds a model to predict human activities such as **Walking**, **Walking Upstairs**, **Walking Downstairs**, **Sitting**, **Standing**, or **Laying** using data from smartphones.
### Dataset
- **UCI HAR Dataset**: Data collected from 30 participants performing daily activities while carrying a smartphone.
### Steps Involved
1. **Importing Libraries**: Libraries like NumPy, Pandas, Matplotlib, Seaborn, and Plotly are used for data handling and visualization.
2. **Data Preparation**:
- Loading features from `features.txt`.
- Importing training and test data from text files.
3. **Model Building**: Applying machine learning techniques for activity classification.
4. **Evaluation**: Assessing the model’s performance using appropriate metrics.
### How to Run
1. Clone the repository.
2. Ensure the **UCI HAR Dataset** is in the correct directory structure.
3. Run the notebook `RPY.ipynb`.
---
## Project 2: Machine Learning Model Evaluation with Blockchain
### Overview
This project implements various machine learning models and deep learning techniques for data analysis. Additionally, it integrates a **blockchain** mechanism to securely store model evaluation metrics.
### Models Implemented
1. **Machine Learning**:
- Logistic Regression
- Support Vector Classifier (SVC)
- Random Forest Classifier
2. **Deep Learning**:
- Neural Network built using TensorFlow and Keras.
### Blockchain Integration
- A custom blockchain class is used to store model performance metrics such as accuracy, precision, recall, and F1-score.
### Key Functions
- **`deep_learning_model()`**: Builds and trains the neural network.
- **`plot_training_history()`**: Visualizes model training accuracy and loss.
- **`evaluate_and_add_to_blockchain()`**: Evaluates the model and adds the results to the blockchain.
### How to Run
1. Clone the repository.
2. Install required dependencies using:
```bash
pip install -r requirements.txt
```
3. Run the notebook `presentation 2.ipynb`.
---
## License
This project is licensed under the MIT License.
## Acknowledgements
- **UCI HAR Dataset** for providing the data for the Human Activity Recognition project.
- TensorFlow, Scikit-learn, and other open-source libraries used in these projects.
---
Feel free to contribute, raise issues, or suggest improvements!