1. Initial setup and Configure the data set.
  2. Load the data set file in variable hotel_data files.
  3. Data set - Hotels : This data comes from an open hotel booking demand dataset from Antonio, Almeida and Nunes.

A list of at least 3 columns (or values) in your data which are unclear until you read the documentation.
There are few columns , has been filter out from data set which were unclear until I read the documentation.
1. meal: The meal column may contain codes or abbreviations for different meal plans offered for given hotel.
2. market_segment: It’s unclear what specific segments or categories the market segment column refers to without consulting the documentation.
3. deposit_type: The column “deposit_type” might contain encoded values indicating the type of deposit required for booking.

(Top five and bottom five sample data from Hotel dataset )

##          hotel meal market_segment deposit_type agent company
## 1 Resort Hotel   BB         Direct   No Deposit  NULL    NULL
## 2 Resort Hotel   BB         Direct   No Deposit  NULL    NULL
## 3 Resort Hotel   BB         Direct   No Deposit  NULL    NULL
## 4 Resort Hotel   BB      Corporate   No Deposit   304    NULL
## 5 Resort Hotel   BB      Online TA   No Deposit   240    NULL
## 6 Resort Hotel   BB      Online TA   No Deposit   240    NULL
##             hotel meal market_segment deposit_type agent company
## 119385 City Hotel   BB  Offline TA/TO   No Deposit   394    NULL
## 119386 City Hotel   BB  Offline TA/TO   No Deposit   394    NULL
## 119387 City Hotel   BB      Online TA   No Deposit     9    NULL
## 119388 City Hotel   BB      Online TA   No Deposit     9    NULL
## 119389 City Hotel   BB      Online TA   No Deposit    89    NULL
## 119390 City Hotel   HB      Online TA   No Deposit     9    NULL
Why do you think they chose to encode the data the way they did? What could have happened if you didn’t read the documentation?
The data may have been encoded to efficiently store categorical information and reduce storage space. Encoding also helps maintain consistency and prevent errors in data entry. Without reading the documentation, one might misinterpret the encoded values, leading to incorrect analysis and conclusions.
  • At least one element or your data that is unclear even after reading the documentation
  • You may need to do some digging, but is there anything about the data that your documentation does not explain?

    • 1.agent The meaning and significance of the ‘agent’ column are unclear. The documentation may not provide sufficient details about the role of agents or their identification numbers. Additionaly upon reviewing dataset for agent, it might require further clarification about the this column as this column is discribed as the ID of the travel agent and It is marked as “NULL” for many entries. It’s unclear whether this signifies bookings made without the involvement of a travel agency or if there’s another reason for the null values. Understanding the significance of these null values could provide insights into the booking process and the role of travel agencies in the dataset. Further exploration or additional documentation may be necessary to clarify this aspect of the data.
    2.company After seeing the dataset, company column was unclear because It is not explicitly defined within the context of the dataset or the provided column descriptions. It’s not clear whether it refers to the company making the booking, the company the guest is associated with, or some other entity. Further clarification or additional documentation may be needed to understand the significance of this column.

    ##   Agent_Present  Count
## 1          TRUE 119390

    Build a visualization which uses a column of data that is affected by the issue you brought up in above. In this visualization, find a way to highlight the issue, and explain what is unclear and why it might be unclear?
    To visualize the issue with the “agent” column where many entries are marked as “NULL”, I have created a bar chart showing the distribution of bookings based on whether It have a non-null value in the “agent” column or not. This will help highlight the proportion of bookings where the agent information is missing.

    Visualization - Presence of Agent Information in Bookings

    Do you notice any significant risks? If so, what could you do to reduce negative consequences?
    According to understaning on dataset for agent column, there are significant risks associated with NULL values in the “agent” column of the dataset
    1. Potential Bias in Analysis: If the “NULL” values represent missing data due to a systematic reason e.g. certain types of bookings not being associated with an agent, the analysis may be biased. For example, if bookings made through certain channels are less likely to have an agent associated with them, any analysis involving the “agent” column may be skewed.
    2. Loss of Information: NULL values in the “agent” column indicate missing information about the booking process. This loss of information could hinder the ability to accurately understand and model factors affecting bookings, cancellations, or other outcomes.
    To reduce the negative consequences of NULL values in the “agent” column, some steps can be taken like
    2.1 Investigate the Reason for NULL Values:
    Insight: It is crucial to understand why “NULL” values are present in the “agent” column. This might involve consulting with data providers, examining data collection processes, or conducting data audits to identify any systematic reasons for missing values.
    Significance: Understanding the reasons for NULL values helps to contextualize their presence in the dataset. It allows us to assess whether the missing data is random or systematic and whether it introduces bias into the analysis.
    Further Questions: 2.2 Impute Missing Values:
    Insight: Depending on the reason for the missing values, it may be possible to impute them using appropriate methods such as mean imputation, mode imputation, or predictive imputation. However, imputation should be done cautiously to avoid introducing bias or inaccuracies into the dataset.
    Significance: Imputation helps maintain the integrity of the dataset and ensures that analyses involving the “agent” column are not biased due to missing data. It allows us to leverage all available information to draw meaningful insights.
    Further Questions:

    2.3 Consider Multiple Perspectives:
    Insight: When analyzing the dataset, it’s essential to consider multiple perspectives and sensitivity analyses to assess the robustness of findings in the presence of missing data. This might involve conducting separate analyses with and without the “agent” column or exploring alternative ways to account for missing values in the analysis.
    Significance: By examining data from different angles and under various assumptions, we gain a more comprehensive understanding of the dataset’s limitations and uncertainties. It helps ensure the reliability and validity of our conclusions.
    Further Questions: 2.4 Document Limitations: Insight: Transparent documentation of data limitations, including the presence of NULL values in the “agent” column, ensures that readers understand the scope and reliability of the analysis.
    Significance: Documenting limitations helps to maintain the integrity and reproducibility of the research findings. It provides transparency about potential biases or uncertainties in the data and analysis methods.
    Further Questions:

    So by addressing these questions and conducting thorough investigations, we can enhance our understanding of the dataset, minimize biases, and ensure the reliability of our analyses and interpretations.

    Thank you !!!!!