The dataset that is provided for this task of data preprocessing, data analysis, and data visualization is related to occupancy hotel statistics, and revenue. The information that is provided is of the Luxury hotel located in Alexandria, VA, which is part of the “larger Washington, DC market”. The set of data contains seventeen columns. In addition to that, each row of this set of data represents a daily update of hotel occupancy as well as metrics revenue for the “Luxury Class submarket located in Alexandria, VA”.
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The columns of the dataset are described here as follows:
Market: it conveys the bigger market to which the hotel in Alexandria, VA belongs.
Class original: it represents the class of hotels within the submarket.
Rooms Supply: it represents the total number of rooms in the hotel that are available in the submarket on the assigned date.
Submarket: it represents the exact submarket within the more extensive market.
Rooms Supply PC: it designates the change of percentage in the whole number of hotel rooms that are available in the submarket compared to the same date in the previous year.
Original Date: it symbolizes the date for which the hotel occupancy and revenue metrics were recorded.
Rooms Supply LY: it represents the absolute number of hotel rooms that are available in the submarket on the same date in the previous year.
Rooms Demand: it means the entire number of hotel rooms that are already occupied in the submarket on the given date.
Rooms Demand LY: it conveys the absolute number of rooms of the hotel settled in the submarket on the same date in the previous year.
Rooms Demand PC: it denotes the percentage change in the real number of hotel rooms settled in the submarket corresponding to the same date in the last year.
Rooms Revenue: it represents the total revenue induced by hotel room sales in the submarket on the assigned date.
Rooms Revenue LY: it represents the entire revenue induced by hotel room sales in the submarket on the same date in the last year.
Rooms Revenue PC: it represents the percentage change in the whole revenue induced by hotel room sales in the submarket compared to the exact date in the last year.
Census Prop Count: it represents the whole number of properties in the submarket on the given date.
Census Room Count: it represents the absolute number of hotel rooms in the submarket on the given date, based on a census.
Sample Prop Count: it represents the entire number of properties in the submarket on the given date, based on a sample.
Sample Room Count: it represents the whole number of hotel spaces in the submarket on the given date, based on a sample.
Section 2: Dataset processing
Preprocessing of data is one of the important steps that help in preparing data for the formation of a model using Machine learning or deep learning techniques. In addition to that, in data preprocessing, several steps are included such as cleaning of data, transformation of data, along with feature selection. The methods of cleaning data and transformation of data are generally utilized for the removal of outliers from a data set and standardizing the data set in a suitable form that can be used for the creation of a model. The prime aim of performing data preprocessing in this project work is to facilitate the process of training or testing by the proper transformation as well as scaling of the total set of data (Jena, 2019). Here, a few steps have been followed for the performance of data preprocessing on the data set of the hotel. At first, data cleaning is done by the replacement of null values as well as cleaning the data set. The practical work for this project is performed with the utilization of software tools “Excel” and “Tableau”.
The requirements for data preprocessing have been followed in this section. It can be seen that the missing values have been identified and necessary processing has been applied to the same. The null values from the dataset have been replaced with “0” and the task has been done using “Microsoft Excel”. The “Date” field has been changed and the appropriate format has been assigned to the column. Corresponding counties and states have been identified based on the dataset and the same can be seen in the revised dataset as well.
Figure 2.1: Replacing the null values with zeros
After the detection of null values in the data set, the null values are replaced with zeroes in order to remove the null values. This is performed by the application of the software tool “Excel”. In the above figure, the replacement of null values with zeroes is visualized.
Figure 2.2: Revised Date
A few preprocessing steps have been addressed in this section along with the procedure mentioned earlier. The “Class” column has been revised so that only the of the class can be seen in the graphical representation. In other words, the “Class” work has been removed from the dataset as well as the variable column. The currency values have also been looked after from the dataset. It can be seen that various currency data have been used for manufacturing the dataset. The use of decimal values can be seen in the column as well. It can be noticed that decimal values up to two points have been taken into account.
Figure 2.3: Revised data for class
The revised date of the data set can be seen in the figure that is attached above. The format of the date is changed to a suitable form for the creation of effective analysis with the implementation of “Excel”.
Figure 2.4: Usage of decimal values in case of currencies
The currencies of the data set are changed into the decimal format by the utilization of data pre-processing methods. This helps in performing analysis most effectively on the data set of the luxury hotel.
Section 3: Dataset analysis
The princess of data set analysis refers to the manipulation of raw data or information in order to uncover the insights of the data set that might be useful for drawing conclusions. Modern analytics of data can be divided into four distinct categories, which are “diagnostic, descriptive, predictive, and prescriptive”. There are a few steps that can be followed to perform data analysis (Akhtar et al. 2020). That is as follows
Identifying the proper Questions
These questions regarding data analysis must be easily measurable as well as closely related to a particular business situation. If the data analysis is performed by a business team, then they should precisely indicate what they are going to do and what the outcome of the analysis.
Breaking down the Data into Segments
It’s usually helpful to split down the dataset into more undersized and defined groups. Segmenting the data will not only make the analysis more effortless, as well as manageable, but will also maintain it on track.
Visualizing the Data
An important part of data analysis is “data visualization”, which refers to the method of making graphical presentations of data (Batt et al. 2020). Visualization of the data is helpful in easily identifying any patterns, trends, or evident outliers.
These steps are followed during the research work for the provision of proper input for analysis of data and visualization of data.
The data analysis section has been addressed after the preprocessing of the data. The data has been analyzed based on the requirements of market analysis for the hotel business in different parts of Washington DC. It can be seen that the “Key Performance Indicators” or KPIs have been identified at the very beginning of the project so that they can be highlighted as much as possible in the data analysis and visualization.
A set of data fields has been created based on the requirements and identification of the KPIs. It has been seen that “Occupancy” is one of the most important factors for the market analysis. It can also be seen that three types of occupancy have been looked after in the project as well as the software implementation. The formula that has been used for the calculation of occupancy is as follows.
“Occupancy = Rooms demand / Rooms Supply”
On the other hand, the occupancy has been represented in terms of percentage and the same can be seen in the dataset as well.
Figure 3.1: Essential KPIs
This figure represents the essential KPIs of the dataset that has been used for the analysis and visualization.
Section 4: Dataset visualization
The data has been visualized with the help of appropriate visualization tools and techniques. It can be seen that the data that has been preprocessed in the previous sections has been used to visualize this dataset. The process has been looked after in this case so that the trends in the data can be identified with ease. It can be seen that the processes and analyzed data have been visualized with the help of “The Tableau” analytical tool so that the inner meaning of the data can be extracted in terms of the trends of the dataset (Advani et al. 2019). The new attached columns have also been taken into account. On the other hand, all the columns have been classified into two classes. All the categorical data has been put into one class and the continuous numerical data has been used to establish the other class.
Figure 4.1: Supply of rooms regarding the market
The supply of the rooms has been displayed based on the market in the image above. It can be seen that a bar plot has been used for the visualization. Washington DC has been supplied with the highest number of rooms and the county d “Montgomery” has been served with the least. A difference in the number can be seen for the supply to the DC and other places (Libardi et al. 2021).
Figure 4.2: Demand the rooms per class
The demand for the rooms classified by the classes has been displayed in the image above and a bar chart has been used for the corresponding visualization. The demand for luxury suits is next to null and that can be a serious concern for the hotel authorities (Pereira, 2022). On the other hand, the upper upscale has been demanded the most among the classes.
Figure 4.3: Revenue from the rooms by date
The revenue from the rooms in terms of dates has been shown in the image above using a line plot. It can be seen that the time has been addressed in terms of years.
Figure 4.4: Demand for the rooms based on submarkets
The demand for the rooms has been identified with the help of an area plot. The same has been done based on the submarkets (Fordham et al. 2019). The peak value of the area curve has been shown in the image as well.
Figure 4.5: Different types of submarkets and their demand
Different types of submarkets, as well as their demands, have been looked after in the process and the same can be seen in the image as well. A line plot has been used to serve the purpose.
Figure 4.6: Different types of supply categorized by the classes
The supply of rooms in different classes has been identified in the process and the same can be seen in the image above. It can be seen that the “Room supply PC” and “Room supply LY” have been taken into account as well (Carroll, 2021).
Section 5: Report
A report has been presented based on the visualization that has been done in the previous section. It can be seen that the data has been displayed with the help of a set of graphical representations so that the inner sights of the data can be identified with ease. It can be seen that all the key findings of the tasks have been identified and listed in this section. The section has been supported by the visualization from the previous section.
Key Findings
The key findings from the market analysis have been stated and discussed in this section. It can be seen that hotel data has been used for analysis and visualization. Thus, the important sections regarding the hotel business have been addressed in the analysis, and the same has been used to find the key aspects of the dataset. It can be seen that the entire market has been classified into submarkets for better analysis. The “Washington DC” submarket has been approached the most by the customers. The approaches have been addressed in terms of demand, revenue, and many more. The luxury classes have been used less and the same has been found in the revenue and demand analysis. All the information has been extracted from the graphical representation, and it can be seen that line, bar, and area plots have been used to fulfil the requirements of the task.
Interpretation of the findings
The findings of the results have been interpreted in this section and it can be seen that a dashboard has been used to do the same.
Figure 5.1: Dashboard
All the findings of the dataset as well as the project have been summarized with the help of a dashboard. It can be seen that the dashboard has been created using the same software as the other graphs and charts. It can also be seen that all the graphical visualizations have been collectively used for the formation of the dashboard, which can be seen in the image above.
A difference in the data can be seen in every graph and chart. This indicates a higher value of standard deviation in the dataset. However, all the relevant columns as well as variables have been used for the visualization as other tasks. On the other hand, a few variations in the demand have been identified based on the submarket.
Suggestions and Recommendations
The authority of the hotel businesses has been suggested and recommended for their growth in the same field. It can be seen that all the suggestions have been done based on the data analysis and the charts that have been developed using the software. The recommendations that have been stated in this section can be implemented for a better business strategy.
The prices of luxury hotel rooms are required to be reduced so that the demand for the same can be increased.
Hotel locations other than Washington DC are required to be looked after as their demand, as well as the revenue, is very low.
A decrease in revenue has been identified in terms of time. The authority is required to look into the matter to solve the possible issues.
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