Dr Venugopala Rao Manneni

Need: When conducting multiple analyses on the same dependent variable, the chance of committing a Type I error increases, thus increasing the likelihood of coming about a significant result by pure chance. To correct for this, or protect from Type

Need The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling

Need: Feature engineering is one of the most important skills needed in data science and machine learning. It has a major influence on the performance of machine learning models and even the quality of insights derived during exploratory data analysis (EDA). What

Need: Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline. Unnecessary features decrease training speed, decrease model interpretability, and, most importantly, decrease generalization performance on

Need  : As outliers impact the statistical analysis , once they detected using univariate or multivariate outlier detection methods , we need to treat them properly  before using them in the  Analysis. Approach:   The following are the some of the

Need: An outlier is a data point in a data set that is distant from all other observations. A data point that lies outside the overall distribution of the dataset. Outliers can drastically change the results of the data analysis

Need:   Before conducting any experiment, we need to select the appropriate and optimum sample size to make sure sample should be representative of the population and the finding are generalized to the population. Also, optimum sample size required To Allow