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Articles by Parth Shukla
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How Does TVF Make Profit Using Data Science
Introduction Most companies and businesses are leveraging and integrating data science and machine learning techniques into their workflow to enhance their sales, marketing, and productivity of the projects and workings on the same. The viral fever, or the TVF, is one of the biggest content creation companies which creates movies, web series, and serials, which is India based company. The TVF uses data science and machine learning techniques to enhance its productivity and user experience. In this article, we will discuss how TVF makes a profit using data science and machine learning, which techniques they might be using, ...
Read MoreThe Role of Trial and Error in Data AnalysisÂ
Introduction Data analysis is an approach in the field of data science and machine learning where the dataset is analyzed well in order to get the relationship between dataset features and get an idea about the behavior of the data and its parameters. In data analysis, trial and error play a major role while developing a machine learning model. It has certain advantages that allow data analysts or data scientists to make the model more reliable and predictive according to the dataset available. In this article, we will discuss the role of trial and error in data analysis, ...
Read MoreHow Does Treating Categorical Variables as Continuous Benefits?
Introduction In machine learning, the performance and accuracy of the model completely depend n the data that we are feeding to it, and hence it is the most influential parameter in model training and model building. Mainly while dealing with the supervised machine learning problems, we have mostly categorical and continuous variables in the dataset. There are some benefits of converting categorical variables into continuous variables. In this article, we will discuss some of the benefits of converting categorical variables to continuous variables, how it affects the model's performance, and what is the core idea behind doing so. ...
Read MoreWhich Evaluation Metrics is Best for Linear Regression
Introduction In machine learning, linear regression is one of the best algorithms used for linear types of data and it returns very accurate predictions the same. Although after training a model with any algorithm it is necessary to check the performance of the algorithm to get an idea about how the model is behaving and what things are needed to improve the model. In this article, we will discuss the various evaluation metrics and the best metric to evaluate the linear regression algorithm. Why Find the Best Evaluation Metrics? There are many evaluation metrics available for regression type of algorithm ...
Read MoreWays to Detect Anomalies in a Given Dataset
Introduction Anomalies are values or data observations that are very different from the other observations in the existing datasets., Detecting and processing the anomalies become essential while building a machine learning model, as the quality of the data that is to be passed to the model should be fair enough to rely on. It is believed that high-quality datasets can give accurate and reliable information and result son even very poor-performing algorithms, and if the quality of the dataset is itself very poor, then there is very less probability of achieving a high-performing model. This article will discuss the outliers, ...
Read MoreModel Validation in Machine Learning
Introduction Model validation is a technique where we try to validate the model that has been built by gathering, preprocessing, and feeding appropriate data to the machine learning algorithms. We can not directly feed the data to the model, train it and deploy it. It is essential to validate the performance or results of a model to check whether a model is performing as per our expectations or not. There are multiple model validation techniques that are used to evaluate and validate the model according to the different types of model and their behaviors. In this article, we will discuss ...
Read MoreMaximum Likelihood in Machine Learning
Introduction Maximum likelihood is an approach commonly used for such density estimation problems, in which a likelihood function is defined to get the probabilities of the distributed data. It is imperative to study and understand the concept of maximum likelihood as it is one of the primary and core concepts essential for learning other advanced machine learning and deep learning techniques and algorithms. In this article, we will discuss the likelihood function, the core idea behind that, and how it works with code examples. This will help one to understand the concept better and apply the same when needed. Let ...
Read MoreHyperparameter Tuning in Machine Learning
Introduction Hyperparameter tuning in machine learning is a technique where we tune or change the default parameters of the existing model or algorithm to achieve higher accuracies and better performance. Sometimes when we use the default parameters of the algorithms, it does not suit the existing data as the data can vary according to the problem statement. In that case, the hyperparameter tuning becomes an essential part of the model building to enhance the model's performance. This article will discuss the algorithm's hyperparameter tuning, advantages, and other related things. This will help one understand the concept of hyperparameter tuning and ...
Read MoreAssumption of Linear Regression - Homoscedasticity
Introduction Linear regression is one of the most used and simplest algorithms in machine learning, which helps predict linear data in almost all kinds of problem statements. Although linear regression is a parametric machine learning algorithm, the algorithm assumes certain assumptions for the data to make predictions faster and easier. Homoscadastocoty is also one of the core assumptions of linear regression, which is assumed to be satisfied while applying linear regression on the respected dataset. In this article, we will discuss the homoscedasticity assumption of linear regression, its core idea, its importance, and some other important stuff related to the ...
Read MoreHow to Select Important Variables from Dataset?
Introduction In machine learning, the data features are one of the parameters which affect the model's performance most. The data's features or variables should be informative and good enough to feed it to the machine learning algorithm, as it is noted that the model can perform best if even less amount of data is provided of good quality. The traditional machine learning algorithm performs better as it is fed with more data. Still, after some value or the quantity of the data, the model's performance becomes constant and does not increase. This is the point where the selection of the ...
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