IndusInd Bank Senior Data Scientist Interview Questions and Answers

t-test is a statistical test used to determine if there is a significant difference between the means of two groups.

• It compares the means of two groups and assesses if the difference is statistically significant.

• It is commonly used in hypothesis testing and comparing the effectiveness of different treatments or interventions.

• There are different types of t-tests, such as independent samples t-test and paired samples t-t...read more

A z-test is a statistical test used to determine whether two population means are significantly different from each other.

• It is used when the sample size is large and the population standard deviation is known.

• The test compares the sample mean to the population mean using the z-score formula.

• The z-score is calculated as the difference between the sample mean and population mean divided by the standard deviation.

• If the ...read more

Linear regression is a statistical method used to model the relationship between two variables.

• It assumes a linear relationship between the dependent and independent variables.

• It is used to predict the value of the dependent variable based on the value of the independent variable.

• It can be used for both simple and multiple regression analysis.

• Example: predicting the price of a house based on its size or predicting the ...read more

Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.

• It is used to predict the probability of a binary outcome (0 or 1).

• It is a type of regression analysis that uses a logistic function to model the relationship between the dependent and independent variables.

• It is commonly used in machine learning and data analy...read more

The formula of logistic regression is a mathematical equation used to model the relationship between a binary dependent variable and one or more independent variables.

• The formula is: log(odds) = β0 + β1x1 + β2x2 + ... + βnxn

• The dependent variable is transformed using the logit function to obtain the log-odds ratio.

• The independent variables are multiplied by their respective coefficients (β) and summed up with the inter...read more

Logistic regression is used for binary classification, while linear regression is used for predicting continuous values.

• Logistic regression is a classification algorithm, while linear regression is a regression algorithm.

• Logistic regression uses a logistic function to model the probability of the binary outcome.

• Linear regression uses a linear function to model the relationship between the independent and dependent vari...read more

AUC-ROC curve is a graphical representation of the performance of a classification model.

• AUC-ROC stands for Area Under the Receiver Operating Characteristic curve.

• It is used to evaluate the performance of binary classification models.

• The curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various classification thresholds.

• AUC-ROC ranges from 0 to 1, with a higher value in...read more

A random forest is an ensemble learning method that combines multiple decision trees to make predictions.

• Random forest is a supervised learning algorithm.

• It can be used for both classification and regression tasks.

• It creates multiple decision trees and combines their predictions to make a final prediction.

• Each decision tree is trained on a random subset of the training data and features.

• Random forest reduces overfittin...read more

I'm seeking new challenges and opportunities for growth in a dynamic environment that aligns with my career goals.

• Desire for professional growth: I'm looking for a role that offers more responsibility and the chance to lead projects.

• Interest in innovative technologies: I want to work with cutting-edge tools and methodologies that can enhance my skill set.

• Cultural fit: I'm seeking a company culture that values collabora...read more

In my key project, I developed a predictive model for customer churn using machine learning techniques to enhance retention strategies.

• Data Collection: Gathered data from various sources, including customer demographics, transaction history, and customer service interactions.

• Feature Engineering: Created features such as average transaction value, frequency of purchases, and customer engagement metrics to improve model ...read more