Capstone Project Tunisia : Monuments and Tourism Agencies

Introduction

As part of my Data Science Capstone Project in these sometimes dark and uncertain times I have decided to consider a case of exploring Tunisia venues and help them improve their customer experience by the means of the following: When I used to live in Tunisia to do my studies I used to have that idea to visit all the cool places in the country.

Data Usage

The data we will be using in the Project are:

• Tunisia government open dataset to get neighbourhoods and their locations
• Foursquare open API for fetching the exact location and addresses of the venues
• Additional data from open sources for monuments list extending

Methodology

In this project, we will use the Foursquare API to explore neighborhoods in Tunisia. We will use the explore function to get the most common venue categories in each neighborhood, and then use this feature to group the neighborhoods into clusters. We will use the k-means clustering algorithm to complete this task. Also, we will use the Folium library to visualize the neighborhoods in Tunisia and their emerging clusters.

Results

• We have fetched the Open Data dataset for tunis
• We have created superimposed map of Tunisia with neighborhoods marked on it
• We used one-hot encoding to explore the categories of the venue by calculating the mean of the frequency of occurrence of each category
• We have also calcuated the frequency for each neighborshood’s venue category
• We have obtained 5 clusters for our neighborhoods and top 10 venues using k-means
• We have used monuments dataframe to create a map and joined the map layer to our existing Tunisia clusters map

Notebook: https://github.com/seifeddinho/Coursera_Capstone/blob/main/TunisiaMonumentsProject.ipynb