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2019 JETIR June 2019, Volume 6, Issue 6www.jetir.org (ISSN23495162)DATA PREPARATION FOR OPTIMIZATION IN DATA MINING Ashish Bhagchandani, Bachelor of Engineering Student: Information Technology Department
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How to fill out data preparation for optimization

01
Identify the objective of the optimization effort.
02
Collect relevant data from various sources.
03
Clean the data by removing duplicates and handling missing values.
04
Transform the data into a suitable format for analysis.
05
Standardize or normalize data if necessary for comparison.
06
Split the data into training and testing sets for validation.
07
Document all steps taken for transparency and reproducibility.

Who needs data preparation for optimization?

01
Data scientists working on machine learning projects.
02
Business analysts performing data analysis.
03
Companies looking to optimize their processes or products.
04
Researchers conducting statistical studies.
05
Organizations aiming to leverage data for strategic decisions.
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Data preparation for optimization involves the process of processing and organizing data to enhance its quality and readiness for analysis. This includes cleaning, transforming, and structuring data to ensure it is suitable for optimization models and techniques.
Typically, organizations and professionals involved in data analytics, data science, or optimization processes are required to file data preparation for optimization. This includes data analysts, data engineers, and business analysts.
To fill out data preparation for optimization, one should identify relevant data sources, clean the data to remove inaccuracies, transform data formats as necessary, and organize the data in a structured format suitable for analysis and optimization algorithms.
The purpose of data preparation for optimization is to ensure that the data is accurate, consistent, and in a usable format, which enables effective analysis and improves the performance of optimization models.
The information reported on data preparation for optimization typically includes data sources, data types, methodology used for cleaning and transforming data, any assumptions made during preparation, and a summary of the final dataset structure.
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