Project: Predict Status of Hotel Booking Using Machine Learning in Python
Project Overview:
In this real-world machine learning project, you will develop a model to predict the status of hotel bookings—whether a booking will be honored, canceled, or no-show. This is a crucial problem for hotels as cancellations and no-shows lead to revenue loss and inefficient resource allocation.
Project Objectives:
Analyze hotel booking data to identify key patterns.
Preprocess data (handling missing values, encoding categorical variables, feature selection).
Train a machine learning model (e.g., Logistic Regression, Random Forest, XGBoost) to classify booking status.
Evaluate model performance using metrics like accuracy, precision, recall, and F1-score.
Deploy the model using Flask/Streamlit for real-world usage.
Dataset:
You can use publicly available datasets such as:
Hotel Booking Demand Dataset (from Kaggle: Hotel Booking Demand)
Technologies Used:
Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)
Machine Learning Models: Decision Tree, Random Forest, XGBoost, or Neural Networks
Flask/Streamlit for deployment
Jupyter Notebook for analysis
Implementation Steps:
Load and explore the dataset (check for missing values, data types).
Data preprocessing (handle missing data, encode categorical features, feature selection).
EDA (Exploratory Data Analysis) (visualize booking trends, cancellation patterns).
Train ML models (split dataset, train models, compare performance).
Optimize the best-performing model (hyperparameter tuning).
Deploy the model using Flask or Streamlit for real-time predictions.
Expected Outcome:
A trained model that predicts if a hotel booking will be canceled or honored.
Insights for hotels to minimize cancellations and maximize revenue.
A web-based interface for users to input booking details and get predictions.