Difference between revisions of "Data Preparation Q2 Sumalika"
| Line 1: | Line 1: | ||
| + | <div style="background:#FFFFFF ; border:#001a66; padding-left:15px; text-align:center;"> | ||
| + | <font size = 5; color="#001a66"><span style="font-family:Century Gothic;">ISSS608: Visual Analytics and Applications</span></font> | ||
| + | <br/> | ||
| + | <font size = 5; color="#001a66"><span style="font-family:Century Gothic;">VAST CHALLENGE 2017 </span></font> | ||
| + | <br/> | ||
| + | <font size = 4; color="#001a66"><span style="font-family:Century Gothic;"> '''- SUMALIKA KODUMURU''' </span></font> | ||
| + | |||
| + | <!--MAIN HEADER --> | ||
| + | {|style="background-color:#001a66;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #000000; background:#001a66; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[Assignment Overview | <font color="#FFFFFF">Assignment Overview</font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; background:#001a66; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[Data Overview| <font color="#FFFFFF"> Data Overview </font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; background:#001a66; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[Question1_SUMALIKA KODUMURU| <font color="#FFFFFF"> Sensor Performance </font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; background:#b30000; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[Question 2_SUMALIKA KODUMURU| <font color="#FFFFFF"> Patterns of Chemical Release </font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; background:#001a66; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[Question 3_SUMALIKA KODUMURU| <font color="#FFFFFF"> Factories Responsible </font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; background:#001a66; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[References & Feedback | <font color="#FFFFFF"> References & Feedback </font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #1B338F; background:#001a66; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | | | ||
| + | |} | ||
| + | </div> | ||
| + | <br/> | ||
| + | <!--MAIN HEADER --> | ||
| + | {|style="background-color:#FFFFFF;" width="100%" cellspacing="0" cellpadding="0" valign="top" border="0" | | ||
| + | |||
| + | | style="font-family:Century Gothic; font-size:100%; solid #000000; background:#FFFFFF; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | [[Question 2_SUMALIKA KODUMURU | <font color="#b30000"> '''Go back to Analysis'''</font>]] | ||
| + | |||
| + | | style="font-family:Century Gothic; font size = 5; solid #1B338F; background:#FFFFFF; text-align:center;" width="16.6%" | | ||
| + | ; | ||
| + | | | ||
| + | |} | ||
| + | |||
| + | |||
''' Data Pre-Processing: ''' | ''' Data Pre-Processing: ''' | ||
Revision as of 12:42, 16 July 2017
ISSS608: Visual Analytics and Applications
VAST CHALLENGE 2017
- SUMALIKA KODUMURU
|
|
|
|
|
|
|
|
|
|
|
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:
