In this project, we have extracted and analyzed approximately 20 insights from a dataset focused on tourism in Tanzania. The primary goal of the project is to understand how much tourists are likely to spend during their visit to Tanzania, based on their characteristics, distribution, organization, and common patterns in trip bookings that are prevalent in tourism. We also examine the impact of various factors such as gender, place of residence, accommodations during the trip, and more on tourism expenditure.
The project is centered around the following key insights:
- Tourist Expenditure: How much money tourists are likely to spend based on their demographics and trip characteristics.
- Tourist Distribution: Analysis of the distribution of tourists based on different characteristics like age, gender, and nationality.
- Booking Patterns: Common patterns in how tourists book their trips, including the types of tours they choose and when they are likely to book.
- Gender Impact: Examination of how gender influences spending habits and travel patterns.
- Residence Impact: Analysis of how the tourists' place of origin impacts their spending and travel behaviors.
- Accommodation Choices: Insights into the types of accommodations tourists prefer and how it affects their overall trip expenditure.
- Google Colab: The primary platform for data analysis.
- Python: Programming language used for data manipulation and analysis.
- Pandas: Library used for data processing.
- Matplotlib/Seaborn: Libraries used for data visualization.