Difference between revisions of "ANLY482 AY2017-18T2 Group03 Data Analysis"
Jytay.2014 (talk | contribs) |
Jytay.2014 (talk | contribs) |
||
| Line 59: | Line 59: | ||
===Outbound Report<br/>=== | ===Outbound Report<br/>=== | ||
... | ... | ||
| − | |||
| − | |||
Revision as of 19:01, 26 February 2018
| HOME | ABOUT US | PROJECT OVERVIEW | DATA ANALYSIS | PROJECT MANAGEMENT | DOCUMENTATION | MAIN PAGE |
| Previous | Current |
|---|
Contents
Methodology
In this section, we will explain the methodology which our team plan to implement to perform analysis on the data provided by our sponsor.
We will be using Python for Exploratory Data Analysis (EDA) to better understand the dataset given and its characteristics. As part of data preprocessing, our team will be performing the following steps to obtain a clean dataset. These steps will eventually be converted into a script which will be used to clean the data file that is uploaded into the dashboard which we will develop for our sponsor.
Data Preprocessing
With every new dataset, we first must clean the data to remove irrelevant data that should not be included in our analysis. For data cleaning, the steps include:
- Handling missing values. If there are missing values in a row of record, the entire row will be excluded because it will be inaccurate to include it.
- Handling duplicate data. Duplicate data could occur when the employees double scan the barcode upon inbound of goods. Similarly, in the event of duplicate data, we will remove the entire row as well.
- Resolving redundancies caused by data integration.
With the clean dataset, we will proceed to further explore the data and find out potential visualizations and analysis that can be done with the dataset to provide a more in-depth analysis and dashboard that will be useful for our sponsor.
| S/N | Data Cleaning Steps | Justification & Rationale |
|---|---|---|
| 1 | ... | ... |
Exploratory Data Analysis
Inbound Report
...
Outbound Report
...
