𝐓𝐨𝐝𝐚𝐲'𝐬 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭 𝐍 𝐀𝐧𝐬

𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐍𝐚𝐦𝐞 - 𝐀𝐜𝐜𝐞𝐧𝐭𝐮𝐫𝐞
𝐑𝐨𝐥𝐞 - 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭

𝐐1. 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐟𝐨𝐫 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬 𝐟𝐨𝐫 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥𝐬?

Ans. Some of the Feature selection techniques are: Information Gain, Chi-square test, Correlation Coefficient, Mean Absolute Difference (MAD), Exhaustive selection, Forward selection, Regularization.


𝐐2. 𝐓𝐫𝐞𝐚𝐭 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐯𝐚𝐥𝐮𝐞𝐬?

Ans. They are:
1. List wise or case deletion
2. Pairwise deletion
3. Mean substitution
4. Regression imputation
5. Maximum likelihood.


𝐐3. 𝐚𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬 𝐮𝐬𝐞𝐝 𝐢𝐧 𝐥𝐢𝐧𝐞𝐚𝐫 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧? 𝐖𝐡𝐚𝐭 𝐰𝐨𝐮𝐥𝐝 𝐡𝐚𝐩𝐩𝐞𝐧 𝐢𝐟 𝐭𝐡𝐞𝐲 𝐚𝐫𝐞 𝐯𝐢𝐨𝐥𝐚𝐭𝐞𝐝?

Ans. 1. Linear relationship.
2. Multivariate normality.
3. no or little multicollinearity.
4. no auto-correlation.
5. Homoscedasticity.

Data to be analyzed by linear regression were sampled violate one or more of the linear regression assumptions, the results of the analysis may be incorrect or misleading.


𝐐4. 𝐇𝐨𝐰 𝐢𝐬 𝐭𝐡𝐞 𝐠𝐫𝐢𝐝 𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐫𝐚𝐧𝐝𝐨𝐦 𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐮𝐧𝐢𝐧𝐠 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲?

Ans. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.


𝐐5. 𝐈𝐬 𝐢𝐭 𝐠𝐨𝐨𝐝 𝐭𝐨 𝐝𝐨 𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝐟𝐢𝐭𝐭𝐢𝐧𝐠 𝐚 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐨𝐝𝐞𝐥?
𝐀ns. Support Vector Machine Learning Algorithm performs better in the reduced space. It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations.


𝐐6. 𝐑𝐎𝐂 𝐂𝐮𝐫𝐯𝐞?
Ans ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. 

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𝐓𝐨𝐝𝐚𝐲'𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭

𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐍𝐚𝐦𝐞 - 𝐂𝐚𝐩𝐠𝐞𝐦𝐢𝐧𝐢
𝐑𝐨𝐥𝐞 - 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭

𝐐1. 𝐩-𝐯𝐚𝐥𝐮𝐞?

𝐀ns. p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.


𝐐2. 𝐈𝐧𝐭𝐞𝐫𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐄𝐱𝐭𝐫𝐚𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧?

𝐀ns. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.



𝐐3. 𝐔𝐧𝐢𝐟𝐨𝐫𝐦𝐞𝐝 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 & 𝐧𝐨𝐫𝐦𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧?

𝐀ns. The normal distribution is bell-shaped, which means value near the center of the distribution are more likely to occur as opposed to values on the tails of the distribution. The uniform distribution is rectangular-shaped, which means every value in the distribution is equally likely to occur.

𝐐4. 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬?

𝐀ns. The recommender system mainly deals with the likes and dislikes of the users. Its major objective is to recommend an item to a user which has a high chance of liking or is in need of a particular user based on his previous purchases.  It is like having a personalized team who can understand our likes and dislikes and help us in making the decisions regarding a particular item without being biased by any means by making use of a large amount of data in the repositories which are generated day by day.

𝐐5. 𝐉𝐎𝐈𝐍 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐒𝐐𝐋

𝐀ns. The SQL Joins clause is used to combine records from two or more tables in a database. 

𝐐6. 𝐒𝐪𝐮𝐚𝐫𝐞𝐝 𝐞𝐫𝐫𝐨𝐫 𝐚𝐧𝐝 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐞𝐫𝐫𝐨𝐫?

𝐀ns. mean squared error (MSE), and mean absolute error (MAE) are used to evaluate the regression problem's accuracy. The squared error is everywhere differentiable, while the absolute error is not (its derivative is undefined at 0). This makes the squared error more amenable to the techniques of mathematical optimization.

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𝐓𝐨𝐝𝐚𝐲'𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭

𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐍𝐚𝐦𝐞 - 𝐓𝐢𝐠𝐞𝐫 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬
𝐑𝐨𝐥𝐞 - 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭

𝐐1. 𝐃𝐞𝐚𝐥 𝐰𝐢𝐭𝐡 𝐮𝐧𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐛𝐢𝐧𝐚𝐫𝐲 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧?

𝐀ns. Techniques to Handle unbalanced Data:
1. Use the right evaluation metrics 
2. Use K-fold Cross-Validation in the right way 
3. Ensemble different resampled datasets 
4. Resample with different ratios 
5. Design your own models


𝐐2. 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧?

𝐀ns. Activation functions are mathematical equations that determine the output of a neural network model. It is a non-linear transformation that we do over the input before sending it to the next layer of neurons or finalizing it as output. 



𝐐3. 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧?

𝐀ns. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.

𝐐4. 𝐖𝐡𝐲 𝐢𝐬 𝐦𝐞𝐚𝐧 𝐬𝐪𝐮𝐚𝐫𝐞 𝐞𝐫𝐫𝐨𝐫 𝐚 𝐛𝐚𝐝 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐨𝐟 𝐦𝐨𝐝𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞?

𝐀ns. Mean Squared Error (MSE) gives a relatively high weight to large errors — therefore, MSE tends to put too much emphasis on large deviations.


𝐐5. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐦𝐮𝐥𝐭𝐢𝐜𝐨𝐥𝐥𝐢𝐧𝐞𝐚𝐫𝐢𝐭𝐲?

𝐀ns. To remove multicollinearities, we can do two things. 
1. We can create new features 
2. remove them from our data.


𝐐6. 𝐥𝐨𝐧𝐠-𝐭𝐚𝐢𝐥𝐞𝐝 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 ?

𝐀ns. A long tail distribution of numbers is a kind of distribution having many occurrences far from the "head" or central part of the distribution. Most of occurrences in this kind of distributions occurs at early frequencies/values of x-axis.


𝐐7. 𝐎𝐮𝐭𝐥𝐢𝐞𝐫? 𝐃𝐞𝐚𝐥 𝐰𝐢𝐭𝐡 𝐢𝐭?

𝐀ns. An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error. 
Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest “good” data, as opposed to truncating them completely.


𝐐8. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐦𝐞𝐝𝐢𝐚𝐧 𝐢𝐬 𝐚 𝐛𝐞𝐭𝐭𝐞𝐫 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐦𝐞𝐚𝐧 ?

𝐀ns. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.


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Today's Interview Quest

Company Name - IBM
Role - Data Scientist

Q1. Assumptions required for carrying out linear regression?

Ans. The regression has five key assumptions:
Linear relationship.
Multivariate normality.
No or little multicollinearity.
No auto-correlation.
Homoscedasticity.


Q2. Pickling and Unpickling?

Ans. Pickling in python refers to the process of serializing objects into binary streams, while unpickling is the inverse of that. It’s called that because of the pickle module in Python which implements the methods to do this.


Q3. Lambda function?

Ans. Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python. This function can have any number of arguments but only one expression, which is evaluated and returned.


Q4. print(len(list1 + list2)). What is the output?

Ans. list1 + list2 gives a new list which is combined both of list1 and list2. so the length will be combined both length, i.e, len(list1) + len(list2).


Q5. Shallow copy and deep copy?

Ans. In Shallow copy, a copy of the original object is stored and only the reference address is finally copied. In Deep copy, the copy of the original object and the repetitive copies both are stored. Shallow copy is faster than Deep copy. The changes made in the copied object also reflect the original object in shallow copy. There is no reflection on the original object when the changes are made in the copied object in deep copy.


Q6. type I and type II error?

Ans. A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.


Q7. Specificity?

Ans. Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives.


Q8. Combat overfitting and underfitting?

Ans. To avoid overfitting: Keep your model simple, Use regularization technique, Use cross-validation. To avoid underfitting: Decrease regularization, increase the training duration.

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Today's Interview Quest

Name - TCS
Role - Data Scientist


Q1. Meaning when the p-values are high and low?

Ans. High P values: your data are likely with a true null. Low P values: your data are unlikely with a true null. A low P value suggests that your sample provides enough evidence that you can reject the null hypothesis for the entire population.



Q2. Missing values of more than 30%, how will you deal with such a dataset?

Ans. Simple approaches include taking the average of the column and use that value, or if there is a heavy skew the median or mode might be better. A better approach, you can perform regression or nearest neighbor imputation on the column to predict the missing values. Then continue on with your analysis/model.



Q3. random forest or multiple decision trees?

Ans. Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. It is based on model ensemble learning technique.


Q4. regularization method?

Ans. Regularization is a technique used for tuning the function by adding an additional penalty term in the error function. Regularization, significantly reduces the variance of the model, without substantial increase in its bias.


Q5. RMSE and MSE ?

Ans. MSE (Mean Squared Error) represents the difference between the original and predicted values which are extracted by squaring the average difference over the data set. It is a measure of how close a fitted line is to actual data points. The lesser the Mean Squared Error, the closer the fit is to the data set. The MSE has the units squared of whatever is plotted on the vertical axis. RMSE (Root Mean Squared Error) is the error rate by the square root of MSE. RMSE is the most easily interpreted statistic, as it has the same units as the quantity plotted on the vertical axis or Y-axis. RMSE can be directly interpreted in terms of measurement units, and hence it is a better measure of fit than a correlation coefficient.

Q6. Can time-series data be declared as stationery? How?

Ans. It is stationary when the variance and mean of the series are constant with time.


Q7. Pruning?

Ans. Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.

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Today's Interview Quest

Company Name - Deloitte
Role - Data Scientist


Q1. Conditional Probability

Ans. Conditional probability formula gives the measure of the probability of an event given that another event has occurred.


Q2. Hypothesis Testing. Null and Alternate hypothesis

Ans. Hypothesis testing is defined as the process of choosing hypotheses for a particular probability distribution, on the basis of observed data Hypothesis testing is simply a core and important topic in statistics.
A null hypothesis is a statistical hypothesis in which there is no significant difference exist between the set of variables. It is the original or default statement, with no effect, often represented by H0 (H-zero). It is always the hypothesis that is tested.

Alternative Hypothesis is a statistical hypothesis used in hypothesis testing, which states that there is a significant difference between the set of variables. It is often referred to as the hypothesis other than the null hypothesis, often denoted by H1 (H-one). The acceptance of alternative hypothesis depends on the rejection of the null hypothesis i.e. until and unless null hypothesis is rejected, an alternative hypothesis cannot be accepted.



Q3. Why use Decision Trees?

Ans. First, a decision tree is a visual representation of a decision situation (and hence aids communication). Second, the branches of a tree explicitly show all those factors within the analysis that are considered relevant to the decision (and implicitly those that are not).


Q4. PCA Advantages and Disadvantages?
Ans.
Advantages: Removes correlated features, Improves algorithm features, Reduces overfitting, Improves Visualization.

Disadvantages: Independent variables become less interpretable, Data Standardization is must, Information loss.


Q5. Central Limit Theorem?

Ans. The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement , then the distribution of the sample means will be approximately normally distributed.


Q6. Over Fitting and Under Fitting

Ans. Overfitting is a modeling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data.


Q7. how to deal with imbalance data in classification modelling?

Ans. Follow these techniques:
1.Use the right evaluation metrics.
2. Use K-fold Cross-Validation in the right way.
3. Ensemble different resampled datasets.
4. resample with different ratios.
5. Cluster the abundant class.
6.Design your own models.

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Today's Interview Quest

Company Name - Philips
Role - Data Scientist


Q1. Time Series (ARIMA)?

Ans. ARIMA, short for ‘AutoRegressive Integrated Moving Average’, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.


Q2. How to reduce overfitting. ?

Ans. Techniques to reduce overfitting:
*Increase training data.
*Reduce model complexity.
*Early stopping during the training phase.
*Ridge Regularization and Lasso Regularization.


Q3. What is precision/recall ratio?

Ans. When it comes to precision we're talking about the true positives over the true positives plus the false positives. As opposed to recall which is the number of true positives over the true positives and the false negatives.


Q4. Dimensionality reduction?

Ans. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.


Q5. Bias and variance?

Ans. Bias is one type of error which occurs due to wrong assumptions about data such as assuming data is linear when in reality, data follows a complex function. On the other hand, variance gets introduced with high sensitivity to variations in training data.


Q6. Difference between classification and clustering?

Ans. In classification data are grouped by analyzing data objects whose class label is known. Clustering analyzes data objects without knowing class label. There is some prior knowledge of attributes of each classification. There is no prior knowledge of attributes of data to form clusters.


Q7. Deal unbalanced classification?

Ans. Techniques to Handle Imbalanced Data
*Use the right evaluation metrics
*Use K-fold Cross-Validation in the right way
*Ensemble different resampled datasets
*Resample with different ratios
*Cluster the abundant class
*Design your own model

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