1. Overview
a. Models Trained
b. Evaluation Metrics Used
2. Motivation
3. Techinical Aspects
a. Project Phases
b. IDe's and Technologies
c. Language and Libraries
This is a Binary classification project, which takes input of multiple Factors that influencing the Hotel Booking-Cancellation.
Models trained
1. Decision Tree
2. RandomForestClassifier (and Tuned RandomForestClassifier),
3. Ada boosting,
4. XGBoosting
5. Gradient Boosting.
Evaluation Metrics Used
1. ROC AUC Curve
2. Classification Reports
1. Recall
2. Precision
3. F1 Score
3. Accuracy Score
Hotel and facility business are booming with profit following the trend of tourism but the only thing that haunts its profitability is the last minute cancellation as it renders the hotels with a lost of opportuity of profit realisation as well as wasted cost room preparation management, which is sometimes rented with great discounts.
Lets Explore the available data.
Project Phases
1. Exploratory Data Analysis
2. Data Defect Correction
3. Data Preprocessing
4. Model Building and Evauation
5. Model Comparision
IDe's and other Technologies
Ide's and Environments
1. Jupyter Notebook
2. Readme.so
3. Git
4. GitHub Project
Language and Librairies
PYTHON
1. Numpy
2. Pandas
3. Sklearn
4. Matplotlib
5. Seaborn
6. pprint
7. Scipy
1. Kaggle
2. Great Learning
3. Towards Data Science
4. Analytics Vidhya
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent