Avkalan.ai Data Scientist Interview Questions and Answers

Hypothesis testing is a statistical method used to make inferences about a population based on sample data.

• It involves formulating a hypothesis about a population parameter, collecting data, and using statistical tests to determine if the data supports or rejects the hypothesis.

• There are two types of hypotheses: null hypothesis (H0) and alternative hypothesis (H1).

• Common statistical tests for hypothesis testing include...read more

Null hypothesis is a statement that there is no significant difference or relationship between variables being studied.

• Null hypothesis is typically denoted as H0 in statistical hypothesis testing.

• It is the default assumption that there is no effect or relationship.

• The alternative hypothesis (Ha) is the opposite of the null hypothesis.

• For example, in a study testing a new drug, the null hypothesis would be that the drug...read more

Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.

• Supervised learning requires labeled data for training

• Unsupervised learning does not require labeled data

• Examples of supervised learning include classification and regression

• Examples of unsupervised learning include clustering and dimensionality reduction

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.

• It assumes a linear relationship between the variables

• It is used to predict the value of the dependent variable based on the independent variable(s)

• It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)

• It is commo...read more

ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Random Forest

• Naive Bayes

• Decision Trees

• Linear Regression

• Logistic Regression

• Neural Networks

• Gradient Boosting

• Clustering Algorithms (K-Means, Hierarchical)

• Association Rule Learning (Apriori)

• Dimensionality Reduction Algorithms (PCA, LDA)

• Reinf