This project aims to develop a machine learning model for recognizing human activities based on sensor data collected from a smartphone. The dataset used in this project is the WISDM dataset, which includes accelerometer and gyroscope data from a Samsung Galaxy S5 smartphone.

Python 3.7 or higher NumPy Pandas Scikit-learn Matplotlib

1. Clone the repository: [git clone https://github.com/y/data_mining.git](https://github.com/johnsonAyo/Data-Visualization.git)
2. Download the Wismd dataset
3. Unzip the dataset
4. Start running the Code Blocks (Best run witrhin a notebook)

The Data Mining and Visualaisation is distributed under the MIT License. See the LICENSE file for more information.

For any questions or inquiries, please contact us at [email protected]

The dataset used in this project contains the following features: Accelerometer data (x, y, z axes) Gyroscope data (x, y, z axes) Activity labels (walking, jogging, sitting, standing,etc) The raw sensor data was collected at a sampling rate of 20Hz and segmented into 10-second non-overlapping windows. From each window, statistical features were derived, such as mean, variance, standard deviation, and correlation between axes.