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I applied via Naukri.com and was interviewed in Aug 2024. There was 1 interview round.
Bias and variance are two types of errors that can occur in a model.
Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.
Variance refers to the error introduced by modeling the noise in the training data, leading to overfitting.
Balancing bias and variance is crucial for creating a model that generalizes well to unseen data.
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I applied via LinkedIn and was interviewed before Jan 2024. There were 4 interview rounds.
Case Study was related to customer propensity to buy.
Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
Assumption of linearity: The relationship between the independent and dependent variables is linear.
Assumption of independence: The residuals are independent of each other.
Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.
Assumption of normality: The resid...
VIF is a measure of multicollinearity in regression analysis, indicating how much the variance of an estimated regression coefficient is increased due to collinearity.
VIF values greater than 10 indicate high multicollinearity
VIF is calculated for each predictor variable in a regression model
VIF is calculated as 1 / (1 - R^2) where R^2 is the coefficient of determination from regressing a predictor on all other predicto
I am impressed by your company's innovative projects and collaborative work culture.
I admire the company's commitment to cutting-edge technology and data-driven solutions.
I am excited about the opportunity to work with a talented team of data scientists and researchers.
Your company's reputation for fostering a collaborative and inclusive work environment is appealing to me.
I applied via Referral and was interviewed before Aug 2022. There were 3 interview rounds.
Retail case study, with soft skills is required for this round
I applied via Approached by Company and was interviewed in Jul 2024. There was 1 interview round.
Python and sql based questions
I applied via Naukri.com and was interviewed in Nov 2021. There were 2 interview rounds.
I applied via Referral and was interviewed before Sep 2022. There were 6 interview rounds.
Had to share my screen and they gave live problems to test my knowledge in python
I was interviewed in Apr 2021.
Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.
For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.
For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.
For SVM, important hyperparameters to tune include ke...
Hyperparameters are settings that control the behavior of machine learning algorithms.
Hyperparameters are set before training the model.
They control the learning process and affect the model's performance.
Examples include learning rate, regularization strength, and number of hidden layers.
Optimizing hyperparameters is important for achieving better model accuracy.
Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.
Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.
LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.
The geometric interpretation of Ridge is that it adds a constraint to the size...
Steps to fit a time series model
Identify the time series pattern
Choose a suitable model
Split data into training and testing sets
Fit the model to the training data
Evaluate model performance on testing data
Refine the model if necessary
Forecast future values using the model
RNN and CNN are neural network architectures used for different types of data.
RNN is used for sequential data like time series, text, speech, etc.
CNN is used for grid-like data like images, videos, etc.
RNN has feedback connections while CNN has convolutional layers.
RNN can handle variable length input while CNN requires fixed size input.
Both can be used for classification, regression, and generation tasks.
Answering a question on data and objective function for cost and revenue optimization case studies.
For cost optimization, look at data related to expenses, production costs, and resource allocation.
For revenue optimization, look at data related to sales, customer behavior, and market trends.
Objective function for cost optimization could be minimizing expenses while maintaining quality.
Objective function for revenue opt...
I was interviewed in Apr 2021.
Round duration - 60 Minutes
Round difficulty - Medium
I was asked two questions in this round . More emphasis was given on the theoretical aspect of the subject in this round .
Hyperparameters of XGBoost can be tuned using techniques like grid search, random search, and Bayesian optimization.
Use grid search to exhaustively search through a specified parameter grid
Utilize random search to randomly sample hyperparameters from a specified distribution
Apply Bayesian optimization to sequentially choose hyperparameters based on the outcomes of previous iterations
Hyperparameters in XGBoost algorithm control the behavior of the model during training.
Hyperparameters include parameters like learning rate, max depth, number of trees, etc.
They are set before the training process and can greatly impact the model's performance.
Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100
Round duration - 50 Minutes
Round difficulty - Medium
This round basically tested some fundamental concepts related to Machine Learning and proper ways to implement a model.
Ridge and LASSO regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.
Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).
LASSO regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).
Ridge regression tends to sh...
Round duration - 50 Minutes
Round difficulty - Medium
This round was based on some basic concepts revolving around Deep Learning .
Outlier values are data points that significantly differ from the rest of the data, potentially affecting the analysis.
Outliers can be identified using statistical methods like Z-score or IQR.
Treatment options include removing outliers, transforming the data, or using robust statistical methods.
Example: In a dataset of salaries, a value much higher or lower than the rest may be considered an outlier.
Round duration - 30 Minutes
Round difficulty - Easy
This is a cultural fitment testing round .HR was very frank and asked standard questions. Then we discussed about my role.
Tip 1 : Must do Previously asked Interview as well as Online Test Questions.
Tip 2 : Do at-least 2 good projects and you must know every bit of them.
Tip 1 : Have at-least 2 good projects explained in short with all important points covered.
Tip 2 : Every skill must be mentioned.
Tip 3 : Focus on skills, projects and experiences more.
I applied via LinkedIn and was interviewed in Oct 2023. There were 3 interview rounds.
SQL coding question. Medium level
Explain my project and then case study regarding launching new apps
I applied via LinkedIn and was interviewed before May 2023. There were 4 interview rounds.
Backpropagation is a method used to train neural networks by adjusting the weights based on the error in the output.
Backpropagation involves calculating the gradient of the loss function with respect to the weights of the network.
The gradient is then used to update the weights in the opposite direction to minimize the error.
This process is repeated iteratively until the network converges to a solution.
Backpropagation i...
1 question on array (sorting related), 1 question on string (hard problem)
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