Merge pull request #11 from trevorki/trevor

Made minor edits to proposal, move team-contract to root

Author: chenzhao299

Proposal.md

@@ -1,17 +1,22 @@
-# Section 1: Motivation and Purpose
+# Proposal
+# 1. Motivation and Purpose
 
-Our role: Data scientist consultancy firm
+**Our role:** Data scientist consultancy firm
 
-Target audience: Internal management
+**Target audience:** Internal management of a hotel company
 
-Better customer understanding and good customer services can improve internal efficiencies resulting in higher revenues. To address this challenge, we propose building a data visualization app that allows the top administration to visually explore the dataset for identifying key characteristics of our customers, missed opportunities (in terms of cancelled reservations) and services ordered by our customers for understanding their needs. Our app will illustrate the key metrics and trends and further allow users to explore different aspects of this data by filtering and re-ordering of different variables in order to understand customer's needs in a better way. 
+Better customer understanding and good customer service can improve internal efficiencies resulting in higher revenues. To help in this goal, we propose building a data visualization app that allows the top administration to visually explore their company's booking data to identify key characteristics of customers, missed opportunities (in terms of cancelled reservations) and services ordered by customers to help understand their needs. Our Super-Hotels-Happy-Manager-Info app will illustrate the key metrics and trends and further allow users to explore different aspects of this data by filtering and re-ordering of different variables in order to better understand customer's needs. 
 
-# Section 2: Description of the data
 
-The data set we used in building the dashboard comes from the Hotel Booking demand datasets from Antonio, Almeida and Nunes at Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal (Antonio, Almeida, and Nunes 2019). The data can be found from the GitHub Repository [here](https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-02-11). There are two sets of real world hotel reservation data contained in this data set, one resort hotel and one city hotel. Each row in this dataset is an individual hotel reservoir information due to arrive between July 1st, 2015 and August 31st, 2017. There are a total of 119,390 booking details with 31 features. 40,060 observations from the resort hotel and 79,330 observations from the city hotel are included in this data set.
-In the preliminary investigation of the data set, we find out there are a total 119,300 booking details with 34% on resort hotels and 66% on city hotels. Each observation has numerical features such as number of adults, number of previous bookings not cancelled etc., and categorical features such as code of room type reserved, type of meal booked etc. In the future work, we would like to select the best features to be displayed in our app in order to deliver an informative dashboard.
+# 2. Description of the data
 
+The data set we used in building the dashboard comes from the *Hotel Booking demand datasets* from Antonio, Almeida and Nunes at Instituto Universit�rio de Lisboa (ISCTE-IUL), Lisbon, Portugal (Antonio, Almeida, and Nunes 2019). The data can be found from the GitHub Repository [here](https://github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-02-11). 
 
-# Section 3: Research questions and usage scenarios
+The data is compased of two smaller datasets each containing reservation data from Super-Hotels in Portugal. One set from hotel in a city and one from a resort hotel. Each row in the dataset is an individual hotel reservation that occured in  between July 1st, 2015 and August 31st, 2017. There are a total of 119,390 booking details with 31 features. 40,060 observations from the resort hotel and 79,330 observations from the city hotel are included in this dataset.
 
-Mary is an Executive Director with the ABC Hotel, Portuguese. and she wants to see the overall trend in the market and what relationships exist among the variables available in the collected data to make better marketing and internal policies. When Mary logs on to the "XXX app", she will see the summary of all the key metrics such as reservations made by year/months to see the seasonality effect on the business. She can also manage better internal resources as per seasonal fluctuation. Also, This will help her in making strategies to attract customers in the off-season. She can also filter trends by locations, type of customers etc. so that she can focus on one segment and can design marketing promotions to allure them. She can see the main factors that are contributing the most in the success story such as the top 5 types of meal ordered. She can also see the effect of one variable on another variable, for example the impact of having kids on demanding extra services.
+Each observation has numerical features such as number of adults, number of previous cancellations etc., and categorical features such as room type reserved, type of meal booked etc. We will select the best features to be displayed in our app in order to deliver an informative dashboard.
+
+
+# 3. Research questions and usage scenarios
+
+Mary is an Executive Director with the Super-Hotels company in Portugal. She wants to see the overall trends in the market and what relationships exist among the variables available in the reservation data to make better marketing and internal policies. When Mary logs on to the Super-Hotels-Happy-Manager-Info app, she will see the summary of all the key metrics such as reservations made by year/months to see the seasonality effect on the business. She can also better manage internal resources to account for seasonal fluctuations in business. This will help her in making strategies to attract customers in the off-season. She can also filter trends by locations, type of customers etc. so that she can focus on one segment and can design marketing promotions to attract them. She can see the main factors that are contributing the most in the success story such as the top 5 types of meal ordered. She can also see the effect of one variable on another variable, for example the impact of having kids on demanding extra services.

team-contract.md

@@ -0,0 +1,61 @@
+# Team Work Contract
+
+We, Cameron Harris, Chen Zhao, Sakshi Jain, Trevor Kinsey, agree with the information documented in our team contract, and will try our best to uphold this charter. By pushing this document in our GitHub repository, we indicate our commitment to our team. All members of the group are to make equal efforts for the success of the group presentation. All group members are to fulfill the responsibilities of completing the assignment and making strong efforts for success related to group role expectations.
+
+## Meeting strategies:
+
+- Come to lectures, labs and group meetings on time (If I miss a group meeting, I will inform the group members ahead of time)
+- Meet every Friday at 2:00 pm PST
+- Meet to make release on Saturday as a group
+- Let the group know of any black out dates, or schedule conflicts you have 3 days in advance
+- If a person depends on your work, aim to have it done a few days early
+
+## Meeting schedules:
+
+| Day       | Start Time  | Duration | Note                                          |
+|-----------|-------------|----------|-----------------------------------------------|
+| Tuesday   | 2:00 pm PST | 2 hrs    | Lab time                                      |
+| Friday    | 2:00 pm PST | 2 hrs    | Zoom info shared in Slack                     |
+| Saturday  | TBD         | TBD      | Scheduled based on project progress on Friday |
+
+## Communications:
+
+- Issues to fix should be stored as a Github Issue
+- Try to have your work reasonably tidy before presenting it to the group to make it easier to understand
+
+## Responsibilities:
+
+- Divide up the work at the start of each week
+- Group will agree on division of work for each milestone
+- Each member should take turns doing all types of work
+- Come prepared to share with my group (If I am unable to prepare for the meeting or a portion of the project, I will make up additional work according to the group needs)
+
+## Attitudes:
+
+- Be open to constructive criticism and be respectful when giving it
+- Celebrate successes
+- It is OK to ask for help if you need it
+
+## Decision making and conflict resolution:
+
+- Major decisions made by vote
+- Use a common toolset so that all parts of the project are accessible to everyone
+- Disputes that need mediation will be mediated by course instructor
+
+## Practical matters:
+
+- Aim for a working version first and improve on the working version iteratively
+- When needed, include brief comments for long, complex chunks of code
+- Include TODO notes as placeholders for future tasks (e.g. docstrings, test cases)
+- When refining others’ codes, check to make sure nothing is broken afterwards and explain why the changes were needed
+
+## Unavailability:
+
+- Trevor is unavailable Wednesday nights and weekends until 2pm
+- Cameron is available all days of the week
+- Chen is available all days of the week
+- Sakshi
+
+## Attribution
+
+Part of this team contract was adapted from Federman Stein, R., & Hurd, S. (2000). Using student teams in the classroom: a faculty guide. Anker: Boston, MA.