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+# The Hotel Dilemma
+- UW FinTech Boot Camp - Project 3 Submission - May 2021
+
+## **Team Members**
+- Monique T.
+- Tony H.
+- April A.
+- Nick N.
+
+## **Motivation**
+
+- After a 1-year pandemic, we all need a vacation...
+- Our group wanted to conduct a project using the tools learned in both Module 1 & Module 2 of the boot camp (python + machine learning). We set out looking for data sets and questions related to supply chain management and demand forecasting, which led us to a very interesting prompt related to forecasting hotel reservation cancellations. Given we all need a good vacation, we pursued the the following project...
+
+## **Research Questions**
+- Using data and machine learning models, can hotel reservation cancellations be predicted?
+- If yes to the above question, what models and methods most accurately predict hotel reservation cancellations?
+
+## **Objectives**
+- Build a ML model that can predict whether a hotel reservation will be cancelled 
+- Analyze and understand data via organization, visualization, and dashboards
+
+## **Data Sources**
+- [Hotel Booking Demand](https://www.kaggle.com/jessemostipak/hotel-booking-demand)
+
+## **Action Items**
+- Data Cleaning & Shaping
+  - Data comes from/affiliated with an article: Hotel Booking Demand Datasets 
+  - Data was cleaned by Thomas Mock and Antoine Bichat (additional cleaning and shaping conducted by our team)
+  - Is there further noise/info we want to weed out? Label encoding?
+- Machine Learning Model
+  - Which model to use (try multiple models)
+  - Ensemble/Classifier/Decision Tree/Regression? Pick several and also apply resampling techniques if needed. We predict classifier models will be the most effective given we will be classifying a binary outcome (cancelled vs not cancelled)
+  - Which parameters/inputs produce the best outcomes (train/test split; different inputs for each ML model type aka reference documentation; which models are most efficient; what features in the dataset can we eliminate)
+  - Look at data in different ways? Is the model/data better for predicting in the summer/winter/fall/etc? Should we try forecasting for specific date ranges, like spring break, holiday breaks, etc. This will be a reach if we have time.
+- Data Visualization 
+  - Visualize by city hotel & resort hotel
+  - Visualize by season
+  - Visualize different demographics
+  - Visualize different ML model outcomes?
+  - Explore other means and methods of visualization that may give unique insight into the data set
+
+## **Work Assignments**
+- ML Models & Workbook - April + Nick
+- Data Visualizations & Dashboard - Monique + Tony
+- Slide Show - Whole Team
+
+## **Technologies**
+- Jupyter lab
+- Python
+- Pandas
+- Numpy
+- Sklearn
+- Pyviz
+- More to be imported and utilized in our python files
+
+## **Attachments**
+- [Analysis Folder](Analysis/final_analysis.ipynb) - ML python files
+- [Visualizations Folder](Visualizations/Dashboard.ipynb) - visualization python files
+- [Data Folder](Data/hotel_bookings.csv)- original data set
+- Final presentation deck
+
+## **Outcomes**
+- Using machine learning models, our Team was able to predict hotel cancellations with confidence (particularly using the SMOTEENN Resampling + BalancedRandomForestClassifier model, which rendered a ~90% accuracy score). Vizualizations of our final accuracy score outcomes are below. Please see our uploaded slide show for more information on the outcomes.
+
+![](Images/accuracy_score_grid.PNG)
+
+![](Images/accuracy_score_viz.PNG)
+
+## **Presentation Assignments**
+- Intro & Hypothesis: Tony
+- Visualizations & Intro to Data: Monique
+- Data Preparation & Model Selection: Nick
+- Model Outcomes & Takeaways: April
+