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Churn rate
Churn rate is the rate at which customers stop doing business with a company over a given period of time. The Company's churn rate provides clarity on how well the business is retaining customers, which is a reflection on the quality of the service the business is providing, as well as its usefulness. For example, a high churn rate or a churn rate constantly increasing over time can be detrimental to a company’s profitability and limit its growth potential. Thus, the ability to predict the churn rate is essential for the company’s success. Many companies rely on predictive analytics that allows creating models that forecast churn rates.
Clustering
Clustering is the task of dividing the data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. Clustering has many applications such as market segmentation, social network analysis, search result grouping, medical imaging, image segmentation, anomaly detection, etc. It is widely used in market research when working with multivariate data from surveys. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers.
Computer Vision
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs, and take actions or make recommendations based on that information. It enables computers to see, identify and process images in the same way that human vision does, and then provide appropriate output. It is like imparting human intelligence and instincts to a computer. The computer must interpret what it sees, and then perform appropriate analysis or act accordingly.
Confusion matrix
A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It is extremely useful for measuring Recall, Precision, Specificity, and Accuracy. It can be applied to binary classification as well as for multiclass classification problems. The following 4 are the basic terminology that will help us in understanding the confusion matrix.
True Positives (TP): when the actual value is Positive and the predicted is also Positive.
True negatives (TN): when the actual value is Negative and the prediction is also Negative.
False positives (FP): When the actual is negative but the prediction is Positive.
False negatives (FN): When the actual is Positive but the prediction is Negative.
Conversion rate
The percentage of people that got converted( i.e reach a specific goal that is defined by the respective company). The goal can be anything like signup, purchase, subscription, etc. The aim of every business is to increase its conversion rate.
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