Bajaj Finserv Data Scientist Interview Questions and Answers

Machine learning algorithms are used to analyze data and make predictions or decisions without being explicitly programmed.

• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms learn from data to improve their performance over...read more

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...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 err...read more

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 le...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'])

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent.

• Gradient descent is used to find the minimum of a function by taking steps proportional to the negative of the gradient at the current point.

• It is commonly used in machine learning to optimize the parameters of a model by minimizing the loss function.

• There are different variants of gradie...read more

LSTM (Long Short-Term Memory) is a type of recurrent neural network designed to handle long-term dependencies.

• LSTM has three gates: input gate, forget gate, and output gate.

• Input gate controls the flow of information into the cell state.

• Forget gate decides what information to discard from the cell state.

• Output gate determines the output based on the cell state.

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

• Mean is the average of a set of numbers, median is the middle value when the numbers are ordered, and mode is the most frequently occurring value.

• Mean is sensitive to outliers, median is robust to outliers, and mode is useful for categorical data.

• T-test is used to compare means of two groups, mean is used...read more

Random Forest is an ensemble learning method used for classification and regression tasks.

• Random Forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the forest makes a prediction, and the final prediction is the average (regression) or majority vote (classification) of all trees.

• Random Forest helps reduce overfitting and improve accuracy compared to a single decision tre...read more