<commit_before>#Human Activity Recognition

##Overview
Human activity recognition is a type of classification problem that involves attaching 
monitoring devices (e.g. accelerometers, heart rate monitors) to a person while that 
person is performing different activities. This study compares the performance of different
classiﬁers on a human activity recognition dataset.

##Files
The following scripts are used for this project:

 - preprocessing.py - separate the raw accelerometer data by subject and activity
 - visualization.py - plot the raw accelerometer data for a specific category of activity
 - RF/record_fetcher_between_subject.py - create input features for between-subject testing for RF/XGBoost
 - RF/record_fetcher_within_subject.py - create input features for within-subject testing for RF/XGBoost
 - RF/rf.py - random forest classifier model
 - RF/xgb.py - XGBoost classifier model
 - LSTM/record_fetcher_between_subject.py - create input features for between-subject testing for LSTM
 - LSTM/record_fetcher_within_subject.py - create input features for within-subject testing for LSTM
 - LSTM/lstm_between_subject - LSTM classifier model for between-subject testing
 - LSTM/lstm_within_subject - LSTM classifier model for within-subject testing<commit_msg>added link to paper<commit_after>#Human Activity Recognition

##Overview
Human activity recognition is a type of classification problem that involves attaching 
monitoring devices (e.g. accelerometers, heart rate monitors) to a person while that 
person is performing different activities. This study compares the performance of different
classiﬁers on a human activity recognition dataset.

For an in-depth write up of this project, see [paper.pdf](https://github.com/iamshang1/Projects/blob/master/Advanced_ML/Human_Activity_Recognition/Paper.pdf).

##Files
The following scripts are used for this project:

 - preprocessing.py - separate the raw accelerometer data by subject and activity
 - visualization.py - plot the raw accelerometer data for a specific category of activity
 - RF/record_fetcher_between_subject.py - create input features for between-subject testing for RF/XGBoost
 - RF/record_fetcher_within_subject.py - create input features for within-subject testing for RF/XGBoost
 - RF/rf.py - random forest classifier model
 - RF/xgb.py - XGBoost classifier model
 - LSTM/record_fetcher_between_subject.py - create input features for between-subject testing for LSTM
 - LSTM/record_fetcher_within_subject.py - create input features for within-subject testing for LSTM
 - LSTM/lstm_between_subject - LSTM classifier model for between-subject testing
 - LSTM/lstm_within_subject - LSTM classifier model for within-subject testing
{'fraction_non_alphanumeric': 0.051508462104488596, 'fraction_numerical': 0.0003679175864606328, 'mean_word_length': 4.822269807280514, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7142872', 'n_tokens_mistral': 716, 'n_tokens_neox': 679, 'n_words': 286}