Freecharge Interview Questions and Answers

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

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 evaluate the performance of the model.

• Accuracy is the prop...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 not pruned properly.

• Random forest is more robust and accurat...read more

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

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 regression is used for tasks like predicting house prices bas...read more

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 represented as y = mx + b, where y is the dependent variable, x...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