Update README.md

README.md

@@ -31,7 +31,7 @@
 - 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)
+  - 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.
   - Were our models better at city hotel (business travel based?) vs. resort hotel (vacation travel based?). This will also be a reach if we have time.
 - Data Visualization