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I applied via Referral and was interviewed before Jan 2020. There was 1 interview round.
Solvency ratio measures a company's ability to meet its debt obligations. Short term ratio focuses on current assets and liabilities, while long term ratio considers long term debt.
Short term solvency ratio is also known as the current ratio
It measures a company's ability to pay off its short term debts with its current assets
Formula: Current assets / Current liabilities
Long term solvency ratio is also known as the deb...
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I applied via Campus Placement and was interviewed before May 2020. There were 3 interview rounds.
I applied via Referral and was interviewed before May 2020. There were 3 interview rounds.
I applied via Referral and was interviewed in Feb 2021. There was 1 interview round.
Outlier analysis involves identifying and treating data points that are significantly different from the rest.
Identify outliers using statistical methods such as box plots, scatter plots, and z-scores.
Determine the cause of the outlier and decide whether to remove it or keep it in the dataset.
Consider the impact of outliers on the analysis and adjust the model accordingly.
Use techniques such as winsorization or data tr...
Multiple imputation can be used to impute missing values by creating multiple datasets with imputed values.
Use multiple imputation to create multiple datasets with imputed values
Combine the results from the multiple datasets to obtain a final imputed dataset
Consider using predictive models to impute missing values
Evaluate the quality of imputation using metrics such as mean squared error or R-squared
Variable selection can be done using techniques like correlation matrix, stepwise regression, and principal component analysis.
Check for correlation between variables using correlation matrix
Use stepwise regression to select variables based on their significance
Perform principal component analysis to identify important variables
Check for multicollinearity using variance inflation factor (VIF)
Consider domain knowledge a...
Variable importance can be tested using various methods such as permutation importance, drop column importance, and SHAP values.
Permutation importance involves randomly shuffling the values of a variable and measuring the decrease in model performance.
Drop column importance involves removing a variable from the model and measuring the decrease in model performance.
SHAP values provide a measure of the contribution of ea...
Logistic Regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.
It is used when the dependent variable is binary (0 or 1).
It estimates the probability of an event occurring based on the values of the independent variables.
It is commonly used in credit risk analysis to predict the likelihood of default.
It can also be used in...
Model performance of classification models can be tested using various metrics.
Use confusion matrix to calculate accuracy, precision, recall, and F1 score.
ROC curve and AUC can be used to evaluate model's ability to distinguish between positive and negative classes.
Cross-validation can be used to test model's performance on different subsets of data.
Use lift charts to compare model's performance with random selection.
U...
Loss function in Logistic Regression measures the difference between predicted and actual values.
It is used to optimize the model parameters during training.
The most common loss function used in logistic regression is the binary cross-entropy loss.
The goal is to minimize the loss function to improve the accuracy of the model.
The loss function is calculated using the predicted probabilities and the actual labels.
Other l...
Xgboost is a gradient boosting algorithm used for classification and regression tasks. It is faster and more accurate than Random Forest.
Xgboost stands for Extreme Gradient Boosting
It is a type of gradient boosting algorithm that uses decision trees
It is faster and more accurate than Random Forest
Xgboost uses a more regularized model formalization to control overfitting
Random Forest builds multiple decision trees and c...
The loss function used in Xgboost is customizable and can be specified by the user.
Xgboost supports various loss functions such as binary logistic regression, multi-class classification, and regression.
The default loss function for binary classification is logistic regression while for regression it is mean squared error.
Users can specify their own loss function by defining a custom objective and evaluation function.
Th...
Xgboost parameters include learning rate, max depth, subsample, colsample by tree, and more.
Learning rate controls the step size during training.
Max depth limits the depth of each tree.
Subsample controls the fraction of observations to be randomly sampled for each tree.
Colsample by tree controls the fraction of features to be randomly sampled for each tree.
Other parameters include min child weight, gamma, and lambda fo
Learning rate controls the step size at each boosting iteration in Xgboost.
Learning rate is a hyperparameter that determines the contribution of each tree in the final output.
A smaller learning rate requires more trees to be added to the model, but can lead to better performance.
A larger learning rate can speed up the training process, but may result in overfitting.
Typical values for learning rate range from 0.01 to 0....
The relationship between two features can be measured using correlation coefficient.
Calculate the correlation coefficient using statistical methods.
Correlation coefficient ranges from -1 to 1.
A positive correlation indicates a direct relationship between the features.
A negative correlation indicates an inverse relationship between the features.
A correlation coefficient of 0 indicates no relationship between the feature
P value is the probability of obtaining a result as extreme or more extreme than the observed result, assuming the null hypothesis is true.
P value is used in hypothesis testing to determine the significance of a result.
A small p value (less than 0.05) indicates strong evidence against the null hypothesis.
A large p value (greater than 0.05) indicates weak evidence against the null hypothesis.
P value should not be used a...
Program to check if 4 coordinates form a square
Calculate distance between all pairs of points
Check if all distances are equal
Check if diagonals are equal
Use Pythagorean theorem to calculate distance
I appeared for an interview before Apr 2023.
Basic Finance questions were there from variety of topicsj
posted on 26 Oct 2023
I applied via Campus Placement and was interviewed in Sep 2023. There were 4 interview rounds.
It was a psychometric , value based and coding test
It was a valued behaviour assessment
It has technical questions
posted on 13 Jul 2023
I applied via Recruitment Consulltant and was interviewed in Jun 2023. There were 3 interview rounds.
EAD is calculated using the formula EAD = Exposure at Default = PD x LGD x EAD
Calculate Probability of Default (PD) based on historical data and credit rating
Determine Loss Given Default (LGD) based on collateral or recovery rate
Use the formula EAD = PD x LGD x EAD to calculate Exposure at Default
Counterparty risk is the risk of default by a party in a financial transaction, while credit risk is the risk of loss due to a borrower's failure to repay a loan.
Counterparty risk is specific to financial transactions involving parties such as banks, brokers, or counterparties in derivatives contracts.
Credit risk is more general and refers to the risk of loss due to a borrower's failure to repay a loan or meet other fi...
I applied via Campus Placement and was interviewed in Feb 2021. There was 1 interview round.
I applied via Company Website and was interviewed in Mar 2018. There were 4 interview rounds.
based on 4 reviews
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