https://github.com/ahmedabdalkreem/hotel-reservation

Our task is to classify a Hotel Reservation as either booking canceled (class1) or no canceled(class0) and use more one model to arrive the best model.
https://github.com/ahmedabdalkreem/hotel-reservation

bagging decisiontreeclassifier ensemble extra-trees-classifier logistic-regression matplotlib numpy pandas python3 random-forest sklearn-library svc-model

Last synced: about 2 months ago
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Our task is to classify a Hotel Reservation as either booking canceled (class1) or no canceled(class0) and use more one model to arrive the best model.

• Host: GitHub
• URL: https://github.com/ahmedabdalkreem/hotel-reservation
• Owner: AhmedAbdAlkreem
• Created: 2023-03-11T09:50:34.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-03-11T10:02:22.000Z (almost 2 years ago)
• Last Synced: 2024-01-29T06:16:47.564Z (11 months ago)
• Topics: bagging, decisiontreeclassifier, ensemble, extra-trees-classifier, logistic-regression, matplotlib, numpy, pandas, python3, random-forest, sklearn-library, svc-model
• Language: Jupyter Notebook
• Homepage:
• Size: 738 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Hotel-Reservation

Our task is to classify a Hotel Reservation as either booking canceled (class1) or no canceled(class0) and use more one model to arrive the best model.

What you will learn before do this project ?

1) Deal with all missing values.

2) Using Encoding and OneHotEncoding to tranform dataset to be numeric.

3) Make scaling to be data in the same range using StandardScaler.

4) Splitting Dataset to tree parts ( Training, Validation and Testing ).

5) Start to set dataset to model.

6) Using almost supervised machine learning model.

7) Example of model use DescionTree, SVC and RandomForest.

8) Start train all model.

9) Calculate the accuracy of train and validation.

10) Choose the best hyperparameter in train and validation.

11) Take the best validataion from all model train.

12) Test the best model.

Before this steps you learn alot know can put you model in real life problem.