Difference between revisions of "Data Preparation Q2 Sumalika"
(Created page with "''' Data Pre-Processing: ''' <br/> '''Dataset:''' Sensor Data <br/> '''Tools & Techniques:''' <br/> 1. JMP <br/> 2. Tableau <br/> 3. Excel <br/> '''1. Check for Missing Va...") |
|||
| Line 49: | Line 49: | ||
[[File:Sumalika_Q1DP2.JPG|100%]] | [[File:Sumalika_Q1DP2.JPG|100%]] | ||
| + | <br/> | ||
| + | |||
| + | |||
| + | '''Data Transformation ''' | ||
| + | <br/> | ||
| + | To identify the correlations between each chemical transform the data using Pivot function in Excel. The data for this plot is transformed as below: | ||
| + | <br/> | ||
| + | [[File:Sumalika_Q2DP1.JPG|100%]] | ||
| + | |||
<br/> | <br/> | ||
Revision as of 10:34, 16 July 2017
Data Pre-Processing:
Dataset: Sensor Data
Tools & Techniques:
1. JMP
2. Tableau
3. Excel
1. Check for Missing Values in the given data set:
The Sensor data set given by VAST and it consists of no missing values.
2. Check for Duplicate Values:
Since there is no unique identifier, no issue of duplicate values.
The date field has 1 value for each, hence no repetitions in date value.
3. Analyse variable distributions:
Date: Refers to date fields for Months April, August and December
Chemical: Almost equal in number with a maximum count of AGOG-3A and minimum count of Methylosmolene
Monitor: A total of 9 sensors are located around the Factories.
Data Transformation
To identify the correlations between each chemical transform the data using Pivot function in Excel. The data for this plot is transformed as below:
