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This was implemented as an assignment for Getting_And_Cleaning_Data course from Coursera

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Getting-And-Cleaning-Data

After downloading the zip file and extracting it, the following steps were carried out:

  1. Each File wasa read in separate variables x_train, x_test, y_train, y_test, subject_train, subject_test, activities, features. All these data were put together into a single dataframe called Merged.

  2. Inorder to select only the mean and standard deviation measures along with the subject and activities code, a select query was framed and a subset was returned to Tidy variable.

  3. For the descriptive activity names instead of activity codes, the activity table is used and replaced accordingly.

  4. For better understandable and readable column names, the abbreviations were replaced.

  5. The dataset from the previous steps were grouped by subject and activity and stored in a separate dataframe-"FinalData" with their average.

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This was implemented as an assignment for Getting_And_Cleaning_Data course from Coursera

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