Linear Discriminant Analysis (LDA) is a supervised learning algorithm used for classification tasks in machine learning. LDA can handle correlation between features in the data. It is a technique used to find a linear combination of features that best separates the classes in a dataset. It is a simple and computationally efficient algorithm and can… Continue reading Linear Discriminant Analysis
Tag: Machine Learning
Principal Component Analysis
An unsupervised learning algorithm technique called Principal Component Analysis (PCA) is used to look at how a set of variables relate to one another. PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. PCA is a dimensionality reduction technique that is reducing the number of variables… Continue reading Principal Component Analysis
Machine Learning in Software Testing
Introduction: Machine learning (ML) has emerged as a transformative force in various industries, and the realm of software testing is no exception. In recent years, the integration of machine learning techniques into software testing processes has shown significant promise in enhancing efficiency, accuracy, and overall quality assurance. This article explores the intersection of machine learning… Continue reading Machine Learning in Software Testing
Basic Libraries Required for Python programming:
1. Pandas: Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables. 2.Numpy NumPy is the fundamental package for scientific computing… Continue reading Basic Libraries Required for Python programming:
Implementing the Naive-Bayes Machine learning Model
It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive Bayesian model is easy to build and particularly useful for very large data sets.… Continue reading Implementing the Naive-Bayes Machine learning Model
DECISION TREE CLASSIFICATION ALGORITHM IN MACHINE LEARNING
INDRODUCTION Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. Why use… Continue reading DECISION TREE CLASSIFICATION ALGORITHM IN MACHINE LEARNING
Stemming: Implementation using Python code
A normalizing method in Python JIRA CODE: JJ-134 Stemming:The idea of stemming is a sort of normalizing method. Many variations of words carry the same meaning, other than when tense is involved.There are mainly two errors in stemming – Over stemming and Under stemming. Over stemming occur when two words are stemmed to same root… Continue reading Stemming: Implementation using Python code
Lemmatization: implementation using Python
For Reducing morphological variations and grouping words to one common root JIRA CODE – JJ-134 It is the process of grouping together the different inflected forms of a word so they can be analysed as a single item. Lemmatization is similar to stemming but it brings context to the words. So it links words with… Continue reading Lemmatization: implementation using Python
K-Nearest Neighbor Algorithm(K-NN Algorithm) in Python
Implementation code of K-NN Algorithm using Python language JIRA CODE – JJ – 134 This algorithm is used to solve the classification model problems. K-nearest neighbor or K-NN algorithm basically creates an imaginary boundary to classify the data. When new data points come in, the algorithm will try to predict that to the nearest of… Continue reading K-Nearest Neighbor Algorithm(K-NN Algorithm) in Python
Naive Bayes Algorithm in Python
Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class. Bayes’ Theorem is stated as: P(class|data) = (P(data|class) * P(class)) / P(data) Where P(class|data) is the probability of class given the provided data. Naive Bayes is a classification algorithm for binary (two-class) and multiclass… Continue reading Naive Bayes Algorithm in Python