Human Activity Recogonition Using Smartphones - KNeighbors Classifier

In this project you will design a robust activity recogonition system based on the smartphones. As you know mobile devices have accelerometer as the sensor which collects the activities. These activities can be classified using K-nearest neighbour.

REQUIREMENT

1. Pandas
2. Numpy
3. Matplotlib
4. Scikit Learn

Data

We will be using a human-activity-recognition-with-smartphones from the Kaggle platform which has a very good collection of datasets.

The file we will be using is present in following directory : input/ It is under license CCO and it is by UCI machine learning repositories

Attribute Information:

For each record in the dataset it is provided:

• Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
• Triaxial Angular velocity from the gyroscope.
• A 561-feature vector with time and frequency domain variables.
• Its activity label.
• An identifier of the subject who carried out the experiment.

License

STEPS :

1. Importing Libraries
2. Exploring the Dataset
3. Exploratory Data Analysis
4. Data Preprocessing
5. Model Building

   • KNeighborsClassifier

6. Evaluation
7. Conclusion

Run

In a terminal or command window, navigate to the top-level project directory Activity_recognition_KNN/ (that contains this README) and run one of the following commands:

ipython notebook Activity recognition_KNN.ipynb or

jupyter notebook Activity recognition_KNN.ipynb

This will open the Jupyter Notebook software and project file in your browser.

Further readings:

• https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
• https://machinelearningmastery.com/how-to-model-human-activity-from-smartphone-data/