Some scikit-learn objects are inherently random. The random_state is used to control the randomness of these scikit-learn objects. The random_state can be an integer, nonNumPynumpy.random.RandomState(seed=None). The random_state parameter will be the predetermined criteria to select the sample from the entire population(dataset). Here all the members of the population have an…

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate the characteristics of the whole population. When their an extremely very large volume of data, it takes too much time and money to gather…

The normal distribution also known as Gaussian distribution is observed across many naturally-occurring measures such as birth weight, height, etc. it is symmetric and bell-shaped. It can range from negative infinity to positive infinity and most of its value clusters around the mean. The portion of the curve below the…

In parametric testing we are well aware of the parameters or assumptions that are made and the population distribution is always known whereas non-parametric testing is a distribution-free test and no assumptions are made. Hypothesis testing is used for both parametric and non-parametric testing. So before going into detail about…

A panel dataset is referred to as longitudinal data or cross-sectional time-series dataset, which, ideally, provides repeated measurements of a certain number of variables over a period on observed units, such as individuals, households, firms, cities, and states. Time-series and cross-sectional data can be thought of as special cases of…

Natural Language Processing refers to the branch of artificial intelligence that gives machines the ability to read, understand and derive meaning from human languages. Robots such as Sophia or home assistants uses Natural Language Processing (NLP) to sound like human and ‘understand’ what you’re saying. NLP can be represented by…

A time series has stationarity when the observations are not dependent on the time. Statistical properties of these time series will not change with time thus they will have constant mean, variance, and covariance. Visualization of a stationary times series will be as below.

Time Series is a series of data points listed or indexed at successive equally spaced points in time. …

Classification in machine learning refers to a predictive modeling problem where a class label is predicted for a given input data. The label or target may belong to two classes or more than two classes. Class imbalance occurs when most of the data belong to one class label. It can…