Machine_learning_project
Machine_learning_and_neural_network_project
This is a project about collecting data in 4 different movement to perform machine learning and neural network.
Explanation of collected data (the data will be used for machine learning and neural network)
I chose running, walking, jumping and bend_knee during the moment of collecting my body data. The data are collected individually by its group. Also, there is another data was collected by combining all movement of myself.
Explanation of machine learning model
I chose KNeighborsClassifier, because this machine learning is classification model. Also, KNN has the property of find how spread are the data surrounding by the K-value. Besides, using grid search can let me to pick my k-value, weight and display the accuracy.
sklearn_ml.html
Results of machine learning.
In the end, the value of count, mean, std, min, 25%, 50%, 75% and max will be display in a table format. Also, finding the correlation
Here is an example of the testing data result
Example of autocorrelation plotfunction
Example of the result of training data
Explanation of neural network model
In this neural network, I used simpleRNN, GRU, LSTM with different layers. Below are the results for LSTM and simpleRNN for 3 different datasets.
tensorflow.html
Results of neural network
traning_data_sets
| neurons | layers | time steps | accuracy |
|---|---|---|---|
| LSTM | 2 | 10 | 0.5993 |
| LSTM | 2 | 20 | 0.5431 |
| LSTM | 2 | 40 | 0.4195 |
| LSTM | 3 | 10 | 0.8436 |
| LSTM | 3 | 20 | 0.8071 |
| LSTM | 3 | 40 | 0.6404 |
| LSTM | 4 | 10 | 0.8146 |
| LSTM | 4 | 20 | 0.6592 |
| LSTM | 4 | 40 | 0.5094 |
| SimpleRNN | 2 | 10 | 0.2603 |
| SimpleRNN | 2 | 20 | 0.4307 |
| SimpleRNN | 2 | 40 | 0.3708 |
| SimpleRNN | 3 | 10 | 0.4522 |
| SimpleRNN | 3 | 20 | 0.3352 |
| SimpleRNN | 3 | 40 | 0.4644 |
| SimpleRNN | 4 | 10 | 0.265 |
| SimpleRNN | 4 | 20 | 0.2622 |
| SimpleRNN | 4 | 40 | 0.2547 |
testing_data_sets
| neurons | layers | time steps | accuracy |
|---|---|---|---|
| LSTM | 2 | 10 | 0.2731 |
| LSTM | 2 | 20 | 0.3981 |
| LSTM | 2 | 40 | 0.2407 |
| LSTM | 3 | 10 | 0.6343 |
| LSTM | 3 | 20 | 0.5093 |
| LSTM | 3 | 40 | 0.444 |
| LSTM | 4 | 10 | 0.287 |
| LSTM | 4 | 20 | 0.2685 |
| LSTM | 4 | 40 | 0.2593 |
| SimpleRNN | 2 | 10 | 0.3056 |
| SimpleRNN | 2 | 20 | 0.2407 |
| SimpleRNN | 2 | 40 | 0.3333 |
| SimpleRNN | 3 | 10 | 0.287 |
| SimpleRNN | 3 | 20 | 0.3519 |
| SimpleRNN | 3 | 40 | 0.2593 |
| SimpleRNN | 4 | 10 | 0.287 |
| SimpleRNN | 4 | 20 | 0.2685 |
| SimpleRNN | 4 | 40 | 0.2593 |
Moments are done continuously
| neurons | layers | time steps | accuracy |
|---|---|---|---|
| LSTM | 2 | 10 | 0.1943 |
| LSTM | 2 | 20 | 0.2759 |
| LSTM | 2 | 40 | 0.4419 |
| LSTM | 3 | 10 | 0.6 |
| LSTM | 3 | 20 | 0.4318 |
| LSTM | 3 | 40 | 0.4186 |
| LSTM | 4 | 10 | 0.3314 |
| LSTM | 4 | 20 | 0.3333 |
| LSTM | 4 | 40 | 0.3953 |
| SimpleRNN | 2 | 10 | 0.0914 |
| SimpleRNN | 2 | 20 | 0.1034 |
| SimpleRNN | 2 | 40 | 0.2093 |
| SimpleRNN | 3 | 10 | 0.3714 |
| SimpleRNN | 3 | 20 | 0.3563 |
| SimpleRNN | 3 | 40 | 0.4884 |
| SimpleRNN | 4 | 10 | 0.3314 |
| SimpleRNN | 4 | 20 | 0.3333 |
| SimpleRNN | 4 | 40 | 0.3256 |