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Machine Learning Articles
Page 26 of 56
Why Ordinary Least Square (OLS) is a Bad Option to Work With?
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 MoreMethods to Select Important Variables from a Dataset
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 MoreHow to Increase Classification Model Accuracy?
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 MoreBuilding a Fraud Detection Model for a Bank
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 MoreHow to Train MFCC Using Machine Learning Algorithms
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 MoreGeorgia Tech MS Degree in CS(Machine Learning) vs. NYU MS Degree in Data Science
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 MoreWhat is Standardization in Machine Learning
A dataset is the heart of any ML model. It is of utmost importance that the data in a dataset are scaled and are within a particular range, to provide accurate results. Standardization in machine learning , a type of feature scaling ,is used to bring uniformity to the datasets , resulting in independent variables and features of the same scale and range. Standardization transforms the standard deviation to 1 and the mean to 0 . In standardization, the mean is subtracted from each data point and the result obtained is divided by the standard deviation , resulting in standardized ...
Read MoreSpaceship Titanic Project using Machine Learning in Python
The original Titanic project in Machine learning is aimed at finding whether a person on the Titanic will survive or not. However, this project named the spaceship Titanic is a bit different. The problem statement here is that a spaceship has people going on a trip in space. But due to a collision, a few people need to be transported to some other dimension or planet. Now this can’t be done randomly. So, we will use a Machine Learning technique in Python to find out who will get transported and who will not. Algorithm Step 1 − ...
Read MoreRainfall Prediction using Machine Learning
The power of machine learning has enabled us to predict rainfall with several algorithms, including Random Forest and XGBoost. There are no best algorithms for predicting rainfall, every algorithm has its advantages and disadvantages. The Random Forest is efficient with small datasets, while the XGboost is efficient with large datasets. In the same way, we can categorise other algorithms based on the needs of our projects. Our goal here is to build a predictive machine-learning model of rainfall based on Random Forests. Algorithm Import all the required libraries such as Pandas, Numpy, Sklearn, and matplotlib. Load the ...
Read MoreMedical Insurance Price Prediction using Machine Learning in Python
Like in many other sectors, predictive analysis is quite helpful in the finance and insurance sector as well. Using this machine learning technique, we can find out useful information about any insurance policy and therefore save huge sums of money. Here, we will be using this approach of predictive analysis for a medical insurance dataset. The problem statement here is that we have a dataset of some people with certain attributes. Using machine learning in Python, we have to find out relevant information from this dataset and also have to predict the insurance price a person will have to ...
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