This story is the capstone project of Udacity Data Visualization Nanodegree. Its main goal is to describe how I’ve improved an existing data visualization from Makeover Monday by combining what I’ve learned throughout the program.

Step 1: Scope the Project and Gather Data

Consequently, the first task was to choose an existing dataset. As I was always very interested in economic fields, so I checked datasets used for MakeoverMonday data then I chosen the Personal Consumption Expenditures on Food and beverages purchased for off-premises consumption.

The original data visualization is here; the dataset with all background information and data dictionary is here.

Step 2: Explore and Assess the Data

The main variable/categories in the original…

Done by: Monirah, Aisha Y Hakami, Lamalharbi and Mohammed Alali


Problem Identification

Developing a recommender system for IPTV in Jawwy service provided by STC. Python and JupyterLab are tools with Sklearn libraries for implementing the proposed recommender system. The aim is to create a business value for a local company to improve their service, and STC was selected for this project with their service Jawwy since they shared its data in the open data initiative.

In this blog, we will build a recommendation model by using the Surprise method

Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.

Surprise was designed with the following purposes in mind:

  • Give users perfect control over their experiments
  • Provide various ready-to-use…

This project was build by Neon team : Aisha Y Hakami, Monirah abdulaziz, Mohammed Al-Ali, and Lamalharbi.

In our daily life when we are shopping online, or looking for a movie to watch, we usually ask the recommendation from our friends for their personal opinion, and when they recommend something that we don’t like but they enjoyed it, what a waste of time right !!. So if there is a system that can understand you, and recommend for you based on your interests, that would be soo cool isn’t it. Well, that precisely what recommender systems are made for.


Problem Statement

Done by: Monirah abdulaziz, Lamalharbi, and Aisha Y Hakami


This article goes through the process of cleaning a dataset which consists of information about the location of the house, price, and other aspects such as square feet, etc. When we work on these sorts of data, we need to modify and aggregate some features to expand their importance and see which column is important for us and which is not. …


With the largest online selection of leading brands in categories such as electronics, fashion, health & beauty, fragrances, grocery, baby products, and homeware, noon is the one-stop-shopping destination for everyone. In this article, I choose the noon website, data was collected from the perfume section for female, male, and kids departments.

This article talks about web scraping and regex to extract required data from this website.

What is Web Scraping?

If you wonder what web scraping is, “Web scraping is a technique of extracting information from websites. …

Done by: Lamalharbi, Aisha Y Hakami and Monirah abdulaziz


In this blog, we will see how we can build a simple content-based recommender system using data, computer books, particularly, this data obtained from Kaggle.

Usually, we plan to buy a book specially the scientific books, we normally ask about the goods ones, research the book domains, compare the books with similar or read the reviews so here the recommender system is the master of this problem.

Recommendation system algorithms:

Content-based recommendation system (CB)

Content-based recommendation systems recommend items to a user by using the similarity of items. This recommender system recommends products or items based on…

Done by: Lamalharbi, Aisha Y Hakami, and Monirah abdulaziz.

src: TitanicShip


This article goes through the whole process of cleaning the data and developing a machine learning model on the Titanic dataset which is one of the famous data sets which every Data Scientist explores at the start of their career and here we are. The dataset provides all the information on the fate of passengers on the Titanic, summarized according to sex, age, economic status (class), and survival.

In addition, you can join the Titanic: Machine Learning from Disaster challenge on Kaggle.

Data Cleaning

# upload the data
train = pd.read_csv('../datasets/train.csv') …


Problem Statement:

Build a Recommendation System for children's books from amazon, a dataset that scrapped by Modhi almannaa, see the link.

This dataset was scraped from Amazon, the largest selling platform, it provides all the information that helps the customers to find the best products, by reviewing all the information the customer needs to know before buying the product. This dataset focused only on children's books.

Recommendation system algorithms:

Content-based recommendation system (CB)

Content-based recommendation systems recommend items to a user by using the similarity of items. This recommender system recommends products or items based on their description or features. It identifies the similarity between the products based on…

Problem Statment

Kingdom of Saudi Arabia (KSA) is the largest country in the Arab states and a member of the “Group of Twenty” (G-20) major world economies. The motor vehicle rate in this country has increased rapidly since the oil boom in the early 1970s, consequently, the number of roads and the transport infrastructures service increased.
In Saudi Arabia car is the main means of transportation, it provides the flexibility and freedom that people really value and want. Road traffic accidents are one of the most critical public health problems worldwide.
The WHO Global Status Report on Road Safety reports that the annual fatality…

Monirah abdulaziz

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