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.ipynb_checkpoints
.ipynb_checkpoints
 
 
24 Hours Depression Machine Learning.ipynb
24 Hours Depression Machine Learning.ipynb
 
 
Condition Data-frame.ipynb
Condition Data-frame.ipynb
 
 
Control Data-frame.ipynb
Control Data-frame.ipynb
 
 
One Week Depression ML.ipynb
One Week Depression ML.ipynb
 
 
Two Week Depression.ipynb
Two Week Depression.ipynb
 
 
condition_1.csv
condition_1.csv
 
 
condition_10.csv
condition_10.csv
 
 
condition_11.csv
condition_11.csv
 
 
condition_12.csv
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condition_dataframe.csv
condition_dataframe.csv
 
 
condition_one_week_dataframe.csv
condition_one_week_dataframe.csv
 
 
condition_wide_dataframe.csv
condition_wide_dataframe.csv
 
 
control_1.csv
control_1.csv
 
 
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control_6.csv
control_6.csv
 
 
control_7.csv
control_7.csv
 
 
control_8.csv
control_8.csv
 
 
control_9.csv
control_9.csv
 
 
control_dataframe.csv
control_dataframe.csv
 
 
control_one_week_dataframe.csv
control_one_week_dataframe.csv
 
 
control_wide_dataframe.csv
control_wide_dataframe.csv
 
 
first_day_condition.csv
first_day_condition.csv
 
 
first_day_control.csv
first_day_control.csv
 
 
one_week_condition_features.csv
one_week_condition_features.csv
 
 
one_week_control_features.csv
one_week_control_features.csv
 
 
scores.csv
scores.csv
 
 
two_week_condition.csv
two_week_condition.csv
 
 
two_week_control_data.csv
two_week_control_data.csv
 
 

About

My final year dissertation project. This project takes motor activity data from a control group and a condition group. The data is filtered, cleaned and transformed for appropriate use to find the "best" classification algorithm to identify depressed patients from non-depressed patients

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classificationdepression-analysisdepression-detectionjupyter-notebookmachine-learningmotor-activitypython
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