RenewBuy Interview Questions, Process, and Tips

Developed a machine learning model to predict customer churn for a telecom company.

• Used supervised learning techniques such as logistic regression and random forests

• Preprocessed data by handling missing values and encoding categorical variables

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

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

• Linear regression aims to find the best-fitting straight line that describes the relationship between variables.

• It is commonly used for prediction and forecasting in various fields such as finance, economics, and social sciences.

• The equation for linear regression is typically repre...read more

Linear regression is used for continuous variables while logistic regression is used for binary classification.

• Linear regression predicts continuous values while logistic regression predicts probabilities.

• Linear regression uses a linear equation to model the relationship between the independent and dependent variables.

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

• Linear reg...read more

Random forest is an ensemble learning method that uses multiple decision trees to make predictions.

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

• Decision tree is a single tree-like structure that makes decisions based on features of the data.

• Random forest reduces overfitting by averaging the predictions of multiple trees.

• Decision tree can be prone to overfitting if no...read more

Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Precision = True Positives / (True Positives + False Positives)

• It is a measure of the accuracy of the positive predictions made by the model.

• A high precision indicates that the model is good at predicting positive cases without many false positives.

Common metrics for linear and logistic regression models are R-squared and confusion matrix respectively.

• For linear regression model, common metric is R-squared which measures the proportion of the variance in the dependent variable that is predictable from the independent variables.

• For logistic regression model, common metric is confusion matrix which includes metrics like accuracy, precision, recall, and F1 score to ...read more

Recall is the ratio of correctly predicted positive observations to the all observations in actual class. F1 is the harmonic mean of precision and recall.

• Recall is calculated as TP / (TP + FN)

• F1 score is calculated as 2 * (precision * recall) / (precision + recall)

• Recall is important in scenarios where false negatives are costly, like in medical diagnosis

Ensemble technique combines multiple models to improve prediction accuracy.

• Ensemble methods include bagging, boosting, and stacking

• Random Forest is an example of ensemble technique using bagging

• Gradient Boosting Machine (GBM) is an example of ensemble technique using boosting

LVM (Logical Volume Manager) is a tool used to manage disk storage in Linux systems by creating logical volumes from physical volumes.

• LVM allows for dynamic resizing of logical volumes without the need to unmount the filesystem.

• It provides features like striping, mirroring, and snapshotting for better data management.

• Commands like pvcreate, vgcreate, lvcreate are used to create physical volumes, volume groups, and logi

yum is a package manager for Red Hat-based systems, while wget is a command-line tool for downloading files from the internet.

• yum is used for installing, updating, and removing packages on Red Hat-based systems

• wget is used for downloading files from the internet, supports HTTP, HTTPS, and FTP protocols

• yum command example: yum install httpd

• wget command example: wget https://example.com/file.zip

Slicing in Python allows you to extract a subset of elements from a list.

• Slicing is done using square brackets and the start:stop:step notation.

• The start index is inclusive, while the stop index is exclusive.

• You can omit any of the three parameters, defaulting to 0 for start, length of list for stop, and 1 for step.

• Negative indices can be used to slice from the end of the list.

• Example: list = [1, 2, 3, 4, 5], list[1:4]