In this blog post, we will identify whether an image is Rock, Paper or Scissors.

In this blog post, we will create a Color Detector that will detect color names and RGB values from any picture using Python and OpenCV. The application will gives us the name of the color when we click on any area in the picture.
We have a data file that contains color names and its RGB values. We will calculate the distance from each color and find the shortest one. We can use an IDE like Spyder or PyCharm for this project.

In this blog post, we will be analyzing the quality of red and white wines, and check which are the attributes that affect wine quality the most.

Imagine you are moving to London, UK. It’s a major metropolitan city, a financial hub, a famous tourist destination, and home to around 9 million people. But as with every big city, crime is a concern, and you would like to live in a neighborhood that is safe and also popular. In this blog, we’ll use the London Crime data and the Foursquare API to select which neighborhood best fits our needs.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can commonly be seen in online stores, movie databases, and job finders. In this blog post, we will explore content-based and colaborative filtering recommendation systems.

In this blog post, we will use Support Vector Machines (SVM) to build and train a model using human cell records, and classify cells as to realize whether the samples are benign or malignant.

In this blog post, we will explore neighborhoods in Toronto, Canada using web scraping and the Foursquare API. We will get the most common venue categories in each neighborhood, and then using the k-means clustering algorithm, group the neighborhoods into clusters.

Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes, i.e. how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against.

In this blog post, we will be looking at Agglomerative Hierarchical Clustering. This is a bottom up approach of hierarchical clustering.

A telecommunications company is concerned about the number of customers leaving their landline business for cable competitors. They need to understand who is leaving. Imagine that you are an analyst at this company and you have to find out who is leaving and why. In this blog post, we will create a model for the telecommunications company using Logistic Regrssion to predict when its customers will leave for a competitor, so that they can take some action to retain the customers.