The data in this project has been provided by partners of Udacity at Bertelsmann Arvato Analytics and represents a real-life data science task.
In this project, we analyzed demographics data for customers of a mail-order sales company in Germany, comparing it against demographics information for the general population. We used unsupervised learning techniques to perform customer segmentation, identifying the parts of the population that best describe the core customer base of the company. Then we used supervised learning to predict which individuals are most likely to convert into becoming customers for the company.
This project aims to try to detect…
The sinking of the Titanic is one of the most unfortunate events in recent history. In this article, we create a machine learning model by using the survival data of this disaster.
RMS Titanic sank on 15 April 1912 in the North Atlantic Ocean, when struck an iceberg. There were 2,224 passengers on board and this disaster resulted in the deaths of more than 1,500 people.
In this article, I will analyze the factors which are important for the survival ratio by using data visualization. After some feature engineering, I will build a machine learning model to predict survived passengers.