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Ahmedabbas75/Candy-Hierarchy-Analysis-Task

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1- About the Dataset :

This is the official survey form for the 2017 Candy Hierarchy. All data collected is anonymous and will be shared with the public as a raw spreadsheet (xml) file. This data is not intended for research purposes, but rather for the writing of a satirical/humour creative non­fiction science piece to be published at BoingBoing.


2- In Mind Questions

  • General Questions related to the existence of

    • missing values?
    • wrong datatypes for columns?
    • complete duplicates in the data?
    • outliers in each column?
    • changed the column names?
    • wrong range of values?
    • columns dropped it?
  • Questions raised during analysis

    • Most people going out or not?
    • What is most common gender?
    • What is mean of people age did the survey?
    • What is most nationality of people did the survey?
    • What is most dress color people prefer that?
    • What is day most people prefer that?
    • What is most website checked out?
    • What is the most loved candy?
    • What is the most hated candy?

3- Conclusion

  • Reached Results from Questions

    • Most people aren't going out
    • Male is the most common gender
    • Most people who did the survey age between 35 and 50
    • Most countries from USA and Canada
    • Most people like dress white and gold
    • Most people preferred friday
    • Science is the most website checked out
    • Full-sized candy bar is the most loved candy
    • Broken glow stick is the most hated candy

💡Summary

  • My main goal was to clean the data so that there are no missing values or duplicates.during that

    • I saw the most and the least loved candy , The most preferred day , how do most of people see the dress and what media site people would prefer to enter.
    • Most people who made this survey are male most people are not going out the average age for those who take the survey is 42 years old most of people who done this survey are from US

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This is a Data Analysis task during learning data science field

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