Introduction A popular statistical method for comprehending and simulating the connections between variables is regression analysis. The dependent variable is frequently assumed to have a normal distribution, though. The accuracy and dependability of the regression model may be jeopardized if this assumption is broken. The Box−Cox transformation offers a potent method for changing skewed or non−normal dependent variables to resemble a normal distribution in order to overcome this issue. We shall examine the Box−Cox transformation theory and use it in regression models in this post. We'll look at the transformation's justification and how it helps to satisfy the ... Read More

Introduction Evaluating machine learning models is a crucial step to determine their performance and suitability for specific tasks. There are several evaluation approaches that can be used to gauge machine learning models, depending on the nature of the problem and the available data. Evaluation Approaches Here are some ideal evaluation approaches commonly used in machine learning: Train/Test Split This strategy aims to imitate real−world situations where the model comes upon fresh, unexplored data. We may determine how effectively a model generalizes to unobserved instances by training it on the training set and then evaluating how ... Read More

Introduction Ordinary least squares is a well−liked and often used method for linear regression analysis (OLS). For data analysis and prediction, however, it is not always the best option. OLS has several limitations and presumptions that, if not properly addressed, might provide biased and false results. The drawbacks and restrictions of OLS will be covered in this article, along with some reasons why it might not be the ideal choice for all datasets and applications. We will also look at additional regression analysis approaches and methodologies that can get around OLS's drawbacks and deliver more accurate and trustworthy findings. ... Read More

Introduction Moment's big data period requires a dependable and effective approach to opting for important variables from datasets. With so numerous functions available, it can be delicate to identify which bone has the most impact on the target variable. opting for only the most important variables improves model performance, improves model interpretability, and reduces the threat of overfitting. This composition describes numerous ways to remove important variables from your dataset. We'll go through both basic statistical approaches like univariate feature selection and regularization, as well as more sophisticated techniques like PCA and feature importance ... Read More

Introduction Two well−liked machine learning techniques, KNN and k−means clustering, are employed for various tasks. Both methods employ the k parameter, but they are applied to distinct problems and work in different ways. During classification and regression problems, KNN is a supervised learning method, whereas k−means clustering is an unsupervised learning approach. We shall examine the main distinctions between KNN and k−means clustering in this article, including the learning style, task, input, distance calculation, output, application, and restrictions of each method. We can select the best algorithm for a task at hand and steer clear of typical ... Read More

Introduction Machine learning largely relies on classification models, and the accuracy of these models is a key performance indicator. It can be difficult to increase a classification model's accuracy since it depends on a number of variables, including data quality, model complexity, hyperparameters, and others. In this post, we'll look at a few methods for improving a classification model's precision. Ways to Increase Accuracy Data Preprocessing Each machine learning project must include data preprocessing since the model's performance may be greatly impacted by the quality of the training data. There are various processes in ... Read More

Introduction Financial fraud has become an increasingly common problem for banks and financial organizations throughout the world as technology advances. Money laundering, identity theft, and credit card fraud can all result in major financial losses as well as damage to a bank's image. As a result, banks must take proactive steps to prevent and detect fraudulent activity. Building a fraud detection model is one such method that can assist identify fraudulent transactions and flag them for further examination. In this article, we will examine the steps involved in creating a fraud detection model for a bank, starting with ... Read More

Introduction Mel Frequency Cepstral Coefficients (MFCCs) is a widely used feature extraction technique for audio processing, particularly in speech recognition applications. A logarithmic compression, a filter bank, and the discrete Fourier transform (DFT) of audio signals in brief time intervals are used to create MFCCs. You will have a thorough understanding of how to train MFCC using machine learning algorithms by the end of this article. What is an MFCC MFCC stands for Mel−Frequency Cepstral Coefficients. It is a widely used feature extraction technique in audio signal processing and speech recognition. The MFCC algorithm is based on the human ... Read More

Introduction Data science and machine learning are fast expanding professions, and having a graduate degree in these topics might provide you an advantage in the employment market. Yet, with so many applications accessible, it might be difficult to select the best one. The MS degree in CS (Machine Learning) from Georgia Tech and the MS degree in Data Science from NYU are two prominent possibilities. The curriculum at Georgia Tech is strongly focused on computer science and machine learning techniques and systems. The curriculum at NYU is more multidisciplinary, covering areas like as statistics, machine learning, data visualisation, and data ... Read More

Machine learning is a subset of artificial intelligence (AI) that enables machines to learn from data without being explicitly programmed. From predicting customer behavior to recognizing images and speech, it is a rapidly growing field. Adding machine learning to your toolkit can help you excel in many sectors such as finance, fraud detection, automobile, research, etc. Day 1-5: Basics of Machine Learning Before diving into its technical aspects, it is imperative to understand the fundamental concepts of machine learning. Learn about the types of machine learning, such as supervised, unsupervised, and reinforcement learning. Focus on key points, ... Read More