TMEIC Interview Questions and Answers

Yes, I have worked on customer segmentation.

• I have used clustering algorithms like K-means and hierarchical clustering to segment customers based on their behavior and demographics.

• I have also used decision trees and random forests to identify the most important features for segmentation.

• I have experience with both supervised and unsupervised learning techniques for customer segmentation.

• I have worked on projects where the goal was to identify high-value customers, churn pred...read more

get_dummies() function in pandas library is used to convert categorical variables into dummy/indicator variables.

• get_dummies() function creates dummy variables for categorical columns in a DataFrame.

• It converts categorical variables into numerical representation for machine learning models.

• Example: df = pd.get_dummies(df, columns=['column_name'])

Developed a predictive model to forecast customer churn for a telecommunications company.

• Identified key features such as customer tenure, monthly charges, and service usage

• Collected and cleaned data from customer databases

• Built a machine learning model using logistic regression or random forest algorithms

• Evaluated model performance using metrics like accuracy, precision, and recall

• Provided actionable insights to reduce customer churn rates

Types of machine learning models include supervised learning, unsupervised learning, and reinforcement learning.

• Supervised learning: Models learn from labeled data, making predictions based on past examples (e.g. linear regression, support vector machines)

• Unsupervised learning: Models find patterns in unlabeled data, clustering similar data points together (e.g. k-means clustering, PCA)

• Reinforcement learning: Models learn through trial and error, receiving rewards or penaltie...read more

Types of errors in statistics include sampling error, measurement error, and non-sampling error.

• Sampling error occurs when the sample does not represent the population accurately.

• Measurement error is caused by inaccuracies in data collection or measurement instruments.

• Non-sampling error includes errors in data processing, analysis, and interpretation.

• Examples: Sampling error - selecting a biased sample, Measurement error - using a faulty measuring device, Non-sampling error -...read more