Porter Business Intelligence Analyst Interview Questions and Answers

Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Q1. Basic Recommendation System Questions
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Q2. A B Testing Questions
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Q1. PROJECT DETAILS
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Q2. SQL Qns
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Q1. PROJECT DETAILS
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Q2. SQL Qnd
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Q1. SQL Basic Questions
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Q2. RCA question if consumption metric goes down
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Given time series data of provider, compute hour wise provider wise no of seconds online

Q1. Case study on the customer churn.
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Q2. Questions on Probability and CLT
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Q1. What are assumptions in Linear Regression
Ans. 
Assumptions in Linear Regression
• Linear relationship between independent and dependent variables

• Homoscedasticity (constant variance) of residuals

• Independence of residuals

• Normal distribution of residuals

• No multicollinearity among independent variables
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Q2. What are overfitting and underfitting
Ans. 
Overfitting and underfitting are two common problems in machine learning models.
• Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data.

• Underfitting occurs when a model is too simple and cannot capture the underlying patterns in the data, resulting in poor performance on both training and new data.

• Overfitting can be prevented by using regularizati...read more
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Q3. How do you improve the performance of Linear Regression
Ans. 
To improve the performance of Linear Regression, you can consider feature engineering, regularization, and handling outliers.
• Perform feature engineering to create new features that capture important information.

• Apply regularization techniques like L1 or L2 regularization to prevent overfitting.

• Handle outliers by either removing them or using robust regression techniques.

• Check for multicollinearity among the independent...read more
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Q4. What are the metrics used to evaluate Linear Regression
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Metrics used to evaluate Linear Regression
• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• R-squared (R²)

• Adjusted R-squared (Adj R²)

• Mean Absolute Error (MAE)

• Residual Sum of Squares (RSS)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)
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Q5. What is Cost function and Error Function
Ans. 
Cost function measures the difference between predicted and actual values. Error function measures the average of cost function.
• Cost function is used to evaluate the performance of a machine learning model.

• It measures the difference between predicted and actual values.

• Error function is the average of cost function over the entire dataset.

• It is used to optimize the parameters of the model.

• Examples of cost functions are ...read more
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Q6. How do you handle Overfitting in Linear Regression
Ans. 
Overfitting in Linear Regression can be handled by using regularization techniques.
• Regularization techniques like Ridge regression and Lasso regression can help in reducing overfitting.

• Cross-validation can be used to find the optimal regularization parameter.

• Feature selection and dimensionality reduction techniques can also help in reducing overfitting.

• Collecting more data can help in reducing overfitting by providing
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Q7. What is the difference between Least Squares Method and the maximum likelihood
Ans. 
Least Squares Method and Maximum Likelihood are both used to estimate parameters, but differ in their approach.
• Least Squares Method minimizes the sum of squared errors between the observed and predicted values.

• Maximum Likelihood estimates the parameters that maximize the likelihood of observing the given data.

• Least Squares Method assumes that the errors are normally distributed and independent.

• Maximum Likelihood does n...read more
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Q8. What is the formula of Logistic Regression
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Logistic Regression formula is used to model the probability of a certain event occurring.
• The formula is: P(Y=1) = e^(b0 + b1*X1 + b2*X2 + ... + bn*Xn) / (1 + e^(b0 + b1*X1 + b2*X2 + ... + bn*Xn))

• Y is the dependent variable and X1, X2, ..., Xn are the independent variables

• b0, b1, b2, ..., bn are the coefficients that need to be estimated

• The formula is used to predict the probability of a binary outcome, such as whether...read more
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Q9. What is Type I and Type II error
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Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.
• Type I error is also known as a false positive

• Type II error is also known as a false negative

• Type I error occurs when the significance level is set too high

• Type II error occurs when the significance level is set too low

• Examples: Type I error - Convicting an innocent person, Type II error - Failing to convi...read more
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Q10. What metrics do you use to evaluate classification models
Ans. 
Metrics used to evaluate classification models
• Accuracy

• Precision

• Recall

• F1 Score

• ROC Curve

• Confusion Matrix
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Q11. How do you handle overfitting and underfitting in Decision Trees
Ans. 
Overfitting in decision trees can be handled by pruning, reducing tree depth, increasing dataset size, and using ensemble methods.
• Prune the tree to remove unnecessary branches

• Reduce tree depth to prevent overfitting

• Increase dataset size to improve model generalization

• Use ensemble methods like Random Forest to reduce overfitting

• Underfitting can be handled by increasing tree depth, adding more features, and reducing regu...read more
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Case Study - How do you improve user engagement of Facebook?
Guesstimates - How many people watched the Squid Game series on Netflix

How do you reduce partner churn in UC?

Easy topics arrays, sequence sum.

Sql based questions on hackerrank.

Q1. Why Swiggy? Where do you see yourself in 5 years?
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Basic problem solving skills were tested along with quantitative questions.

Q1. Puzzles, Guestimate and Problem Solving skills were tested along with SQL
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