This document summarizes a study that used Python and Pandas to analyze the correlation between hotel room prices and customer voting scores in Thailand. The study imported hotel data from an undisclosed source into a Pandas dataframe. It used functions like Describe() and quantile() to clean the data by removing outliers. A scatter plot was created to visualize the relationship between price and voting scores. The plot showed most hotels had prices between 500-1500 Thai Baht and received average scores above 7, suggesting higher prices between 500-3000 Baht may lead to higher votes.

1. CORRELATION BETWEEN
HOTEL PRICE V. VOTING
WITH PYTHON AND PANDAS
BY APIPOJ PIASAK

2. USING PYTHON AND PANDAS
CORRELATION BETWEEN HOTEL PRICE V.
VOTING
• Sample of using Data Science
in a real-world data
• How to apply use scatter plot
to find a relationship between
Room price and voting.
• To check weather the price
range cause the high voting?

3. HOTEL Data in Thailand
(Source of data can't be disclosed)

4. Import *.csv data using Pandas
Run the code in IPYTHON Notebook

5. Using pandas Describe() function to check outliers with percentiles
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6. Remove unused column and calculate quantile()
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7. Using Apply function to filter value based on percentiles value
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8. Check data frame again, data looks much better now.
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9. Check data frame again, data looks much better now.
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10. BONUS (1)
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11. BONUS (2)
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12. FROM OUR DATA PLOT
CONCLUSION
• The relationship between
price and voting, if you want
to get higher vote, you may
think about your price range
should be between 500THB -
3000THB, hmmm interesting.

13. FROM OUR DATA PLOT
CONCLUSION
• Most of Thailand hotel has
price range between 500THB-
1500THB

14. FROM OUR DATA PLOT
CONCLUSION
• Seems like, the customer who
have booked any hotel vote at
least 7 score.
• This means if any vote is
beyond 7 it is bad! you have
to improve now!

15. — Carly Fiorina, former executive of Hewlett-Packard Co.
THE GOAL IS TO TURN
DATA INTO INFORMATION,
AND INFORMATION INTO
INSIGHT.
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