This is a data analysis project that aims to explore and analyze hotel bookings data to uncover insights and patterns. The data used in this project was obtained from Kaggle and contains information on hotel bookings from two hotels: A city hotel and A resort hotel.
The project involves the following steps:
1. Data Cleaning and Preparation
2. Exploratory Data Analysis
3. Visualization and Insights
The first step in this project involves cleaning and preparing the data. This includes checking for missing data, removing duplicates, and converting data types. Some of the specific tasks involved in this step include:
- Handling missing data
- Removing duplicates
- Converting data types
- Handeling Categorical Variables
The next step in the project is to conduct exploratory data analysis.
This involves examining the data to understand its distribution, central tendencies, and correlations between variables.
Some of the specific tasks involved in this step include:
- Examining the distribution of numerical variables
- Examining the relationship between variables
- Identifying patterns and trends in the data
- Identifying the factors that influence booking cancellations
The final step in the project is to create visualizations and derive insights from the data.
This involves creating graphs and charts to help understand the data and communicate the findings to others.
Some of the specific tasks involved in this step include:
1. Creating visualizations such as histograms, scatter plots, and bar charts
2. Deriving insights from the data
3. Communicating the findings to others
4. The project concludes with a summary of the findings and recommendations for future analysis.
- pandas
- numpy
- matplotlib
- seaborn
- geocoder
- plotly
- prettytable
- This project was completed as part of the Alma Better Full Stack Data Scientist program