This project aims to analyze hotel bookings data to predict which hotel stays included children and/or babies based on various characteristics of the stays. The analysis and prediction are divided into three main parts:

1. Exploratory Data Analysis (EDA)
2. Feature Extraction and Model Training
3. Presentation of Key Findings and Model Results

The project directory is organized as follows:

1. Clone the repository:

   git clone <https://github.com/yamil-abraham/fligoo_assessment.git>
   cd fligoo_assessment

2. Create a virtual environment and activate it:

   python -m venv env
   
   source env/bin/activate  

env\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

1. Exploratory Data Analysis (EDA):

   • Open the Jupyter notebook fligoo_take_home_data_scientist_semi_senior.ipynb.

   • Run the cells to perform EDA and visualize the key findings.

2. Feature Extraction and Model Training:

   • The feature extraction and model training are implemented in the pipelines.py and transformers.py files.

   • Run the notebook cells to execute the pipeline and train the models.

3. Presentation of Key Findings:

   • The final report summarizing the key findings and model results is available in conclusions/final_report.md.

For any questions or further information, please contact [Yamil Abraham] at [[email protected]].

This README provides an overview of the project. For detailed analysis and results, please refer to the Jupyter notebook and the final report in the conclusions directory.