Rachit Mehra & Co Interview Questions and Answers

I rate myself 8/10 in Python. I have experience in data manipulation, visualization, and machine learning.

• Proficient in Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn

• Experience in data cleaning, preprocessing, and feature engineering

• Developed machine learning models for classification, regression, and clustering

• Familiar with deep learning frameworks such as TensorFlow and Keras

• Implemented neural networks for image classification and natural language proc...read more

Logit regression is a statistical method used to model binary outcomes.

• Logit regression is used when the dependent variable is binary (0 or 1).

• It models the probability of the dependent variable taking the value 1.

• It uses the logistic function to transform the linear regression equation into a probability.

• It is a type of generalized linear model (GLM).

Validation of a model involves testing its performance on new data to ensure its accuracy and generalizability.

• Split data into training and testing sets

• Train model on training set

• Test model on testing set

• Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score

• Repeat process with different validation techniques such as cross-validation or bootstrapping

R2 and adj R2 are statistical measures used to evaluate the goodness of fit of a regression model.

• R2 measures the proportion of variance in the dependent variable that is explained by the independent variable(s).

• Adjusted R2 is a modified version of R2 that takes into account the number of independent variables in the model.

• R2 ranges from 0 to 1, with higher values indicating a better fit.

• Adjusted R2 can be negative if the model is worse than a simple mean model.

• Both R2 and ad...read more

Linear Regression predicts continuous values while Logistic Regression predicts binary outcomes.

• Linear Regression is used for predicting continuous values while Logistic Regression is used for predicting binary outcomes.

• Linear Regression uses a linear approach to model the relationship between dependent and independent variables while Logistic Regression uses a logistic function to model the probability of a binary outcome.

• Linear Regression assumes a normal distribution of er...read more