FE fundinfo Data Analyst Interview Questions and Answers

Logistic Regression is a statistical method used to model the probability of a certain event occurring based on one or more predictor variables. Recall is a metric used to evaluate the performance of a classification model.

• Logistic Regression is used for binary classification tasks, where the outcome variable is categorical and has two classes.

• It estimates the probability that a given input belongs to a certain categor...read more

Recall is a metric used in classification models to measure the ability of the model to find all relevant cases within a dataset.

• Recall is calculated as the number of true positive predictions divided by the sum of true positive and false negative predictions.

• It is also known as sensitivity or true positive rate.

• A high recall indicates that the model is good at identifying all relevant cases, even if it means more fals...read more

Linear regression is used for predicting continuous values, while logistic regression is used for predicting binary outcomes.

• Linear regression is used when the target variable is continuous and has a linear relationship with the independent variables.

• Logistic regression is used when the target variable is binary or categorical and the relationship between independent variables and the target is non-linear.

• Linear regres...read more

Performance metrics of Regression include Mean Squared Error, R-squared, Mean Absolute Error, etc.

• Mean Squared Error (MSE)

• R-squared (R^2)

• Mean Absolute Error (MAE)

• Root Mean Squared Error (RMSE)

• Adjusted R-squared

My current work involves analyzing transaction data to identify patterns and trends, which can help PayU optimize their payment processing services.

• Analyzing transaction data to identify fraudulent activities and improve security measures for PayU

• Developing predictive models to forecast transaction volumes and optimize payment processing times

• Utilizing machine learning algorithms to personalize user experiences and inc...read more

The project architecture consists of multiple components that work together to process and analyze data.

• The architecture follows a distributed computing model.

• Data is collected from various sources and stored in a data lake or data warehouse.

• Data processing and transformation tasks are performed using tools like Apache Spark or Hadoop.

• Processed data is then loaded into a database or data store for further analysis.

• Anal...read more

Our architecture lacks scalability and real-time processing capabilities.

• Our current architecture is not designed to handle large volumes of data.

• Real-time processing of data is not possible with our current setup.

• We lack a proper data pipeline for efficient data processing.

• Our architecture is not fault-tolerant and lacks redundancy.

• We need to improve our data storage and retrieval mechanisms.

• Our architecture does not ...read more