Introduction The average value of a set of data points, observations, or values is known as the Mean of the data. It is the measure of the central tendency. Mathematically, the mean is obtained by dividing the sum of values by the number of values or observations. It is ... Read More

Introduction Regression is the technique of predictive modeling to analyze the relationship between the independent variable and the dependent variable. The relation between the target(dependent variable) and the independent variable may be either linear or non-linear. The target is always continuous value and regression is widely used in forecasting, understanding ... Read More

Introduction A single token is called a unigram. A unigram tagger is the type of tagger that requires only one word for inferring the Parts of Speech of a word. It has the context of a single word.NLTK library provides us with the UnigramTagger and is inherited from NgramTagger. In ... Read More

Introduction In NLP tokenizing text into sentences is a very crucial preprocessing task. Tokenization is the process of breaking the text corpus into individual sentences. In NLTK, the default tokenizer does a good task to tokenize text however it fails to do so in cases where the text contains punctuations, ... Read More

Introduction WordNet is a large database of words present in the NLTK library in present in many languages for Natural Language related use cases. NLTK library has an interface known as Synset that allows us to look for words in WordNet. Verbs, Nouns, etc. are grouped into sunsets. WordNet and ... Read More

Introduction K-Medoids is an unsupervised clustering algorithm using the partition method of clustering. It is an improvised version of the K-Means clustering algorithm especially used to handle outlier data. It requires unlabeled data to work with. In this article let us understand the k-Medoids algorithm with an example. K-Medoids Algorithm ... Read More

In today’s date, Artificial Intelligence has impacted our lives way in that nothing else could have impacted. It has changed how our daily jobs are done and has involvement in major areas of our industry, lives, and almost everything. Here in this article, we are going to see some areas ... Read More

Introduction The distribution of data gives us useful insights into the data while working with any data science or machine learning use case. Data Distribution is how the data is available and its present condition, the information about specific parts of the data, any outliers in the data as well ... Read More

Introduction Categorical data is a type of data that takes a fixed number of values and there is no possible logical order in such variables. Categorical variables can be blood groups, yes-no situations, gender, ranking (ex. first, second, third), etc. Categorical variables most of the time undergo encodings such as ... Read More

Introduction Machine Learning is a diverse area with a wide range of applications in many domains. As far as we have an abundance of usable data and a problem to solve in a particular area, Machine Learning can do wonders. Machine Learning Projects can vary in terms of complexity from ... Read More