Differentiate between univariate, bivariate and multivariate analysis.

Univariate analysis are descriptive statistical analysis techniques which can be differentiated based on one variable involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.

The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.

Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.

Comments

15 Common questions for Machine Learning...!!

1. What is logistic regression? Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modelled using a logistic function. 2. What is the syntax for logistic regression? Library: sklearn.linear_model.LogisticRegression Define model: lr = LogisticRegression() Fit model: model = lr.fit(x, y) Predictions: pred = model.predict_proba(test) 3. How do you split the data in train / test? Library: sklearn.model_selection.train_test_split Syntax: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) 4. What is decision tree? Given a data of attributes together with its classes, a decision tree produces a sequence of rules that can be used to classify the data. 5. What is the syntax for decision tree classifier? Library: sklearn.tree.DecisionTreeClassifier Define model: dtc = DecisionTreeClassifier() Fit model: model = dtc.fit(x, y) Predictions: p...

Introduction to Datascience

Data Science has become one of the most demanded jobs of the 21st century. What is Data Science? “Data Science is about extraction, preparation, analysis, visualization, and maintenance of information. It is a cross-disciplinary field which uses scientific methods and processes to draw insights from data. ” As a data scientist, you take a complex business problem, compile research from it, creating it into data, then use that data to solve the problem. A Data Scientist, specializing in Data Science, not only analyzes the data but also uses machine learning algorithms to predict future occurrences of an event. Therefore, we can understand Data Science as a field that deals with data processing, analysis, and extraction of insights from the data using various statistical methods and computer algorithms. It is a multidisciplinary field that combines mathematics, statistics, and computer science. Why Data Science? So, after knowing what exactly Data Science is, you must explore ...

Blog | Data Science and Technology

Search This Blog