A data set is called imbalanced if it contains many more samples from one class than from the rest of the classes. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class. …
As per the Gartner research, in 2020, there will be 20.4 billion connected “things” in use, up from 8.4 billion in 2017. From smartphones to sensors to appliances to stoplights, the exponential growth of connected devices is creating an overwhelming amount of data about what we do, how we do it, and where it happens. A huge amount of the data that companies collect has a spatial component. …
Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. For machine learning, every dataset does not require normalization. It is required only when features have different ranges.
For example, consider a data set containing two features, age(x1), and income(x2). Where age ranges from 0–100, while income ranges from 0–20,000 and higher. Income is about 1,000 times larger than age and ranges from 20,000–500,000. So, these two features are…
A few days back, I was building a Deep Neural Network model using keras for predicting Telecom Customer Churn. But, before building a model, the most important step one needs to perform is data preprocessing. And one task of data preprocessing which comes often during Machine Learning and Deep Learning is converting categorical data into numeric data. Because, neural network model will not convert a string to float, and error will be thrown at the time of model fitting. Below is the error displayed:
There are myriad methods to handle the above problem. One of the methods to create dummy…
