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Programming Quiz

Study the code below:

word = "cloudyml"
print(*word)

Today's Interview Quest N Ans

Company Name - Tiger Analytics

Role - Data Scientist
___________________

Q1. Filter and wrapper feature selection methods?

Ans. Wrapper methods measure the “usefulness” of features based on the classifier performance. In contrast, the filter methods pick up the intrinsic properties of the features (i.e., the “relevance” of the features) measured via univariate statistics instead of cross-validation performance. So, wrapper methods are essentially solving the “real” problem.
Filter methods: information gain, chi-square test, fisher score, correlation coefficient, variance threshold
Wrapper methods: recursive feature elimination, sequential feature selection algorithms, genetic algorithms


Q2. C and degree in SVM?

Ans. The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. degree: It is the degree of the polynomial kernel function ('poly') and is ignored by all other kernels. The default value is 3.


Q3. Dbscan vs kmeans?

Ans. In Data Science and Machine Learning, KMeans and DBScan are two of the most popular clustering(unsupervised) algorithms. Density clustering algorithms use the concept of reachability i.e. how many neighbors has a point within a radius. DBScan is more lovely because it doesn’t need parameter, k, which is the number of clusters we are trying to find, which KMeans needs. When you don’t know the number of clusters hidden in the dataset and there’s no way to visualize your dataset, it’s a good decision to use DBScan. DBSCAN produces a varying number of clusters, based on the input data.


Q4. adjusted R2 and R2?

Ans. R2 is statistical measurement used to explain the dependent and independent variables. Adjusted R Squared is a measurement that predicts the regression variables. This model will take additional input variable that predicts to solve the problems. Adjusted R2 is the better model when you compare models that have a different amount of variables.


Q5. How to decide imputation method if there are null values in a dataset?

Ans. Mean/Median Imputation:-the mean or a median value of a variable is used in place of the missing data value for that same variable. Median over mean when the data column has any outliers.
Mode substitution:- the highest occuring value for categorical value is used in place of the missing data value of the same variable.
Deleting Column: If the number of null values are more than 70%, then prefer deleting the column as it doesn't contribute to the model.

Q5. P value and it’s significance?

Ans: The p-value is the probability that the null hypothesis is true. (1 – the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.

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How much math is required for Data Science ?

Today's Interview Quest N Ans

Company Name - EY

Role - Data Scientist
_________________

Q1. What is Yolo?

Ans. YOLO - You Only Look Once is an algorithm proposed by by Redmond et. al in a research article published at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) as a conference paper, winning OpenCV People’s Choice Award.

Compared to the approach taken by object detection algorithms before YOLO, which repurpose classifiers to perform detection, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.


Q2. Select perfect k for k means

Ans. There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.


Q3. Model metrics when you have outliers?

Ans. The bigger the MAE, the more critical the error is. It is robust to outliers. Therefore, by taking the absolute values, MAE can deal with the outliers.


Q4. Features for food delivery data to give discount to selected customers?

Ans. The features for same can be: Total number of orders, Frequency of ordering per week, Amount paid per order, Distance travelled by delivery man etc.

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#quiz

Study the code below:

print (6 + 5 - 4 * 3 / 2 % 1)

Understand the difference between Tensorflow and PyTorch and which one will be best for You

Click Here👉 https://bit.ly/Tensorflow-or-PyTorch

Today's Interview Quest N Ans

Company Name - Hotstar

Role - Data Analyst
_____________________

Q1. What is the statistical power of sensitivity?

Ans. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (α) if a true effect of a certain magnitude (MEI) is in fact present.


Q2. What is the difference between covariance and correlation?

Ans. “Covariance” indicates the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables.


Q3. What is negative indexing? Why is it needed? Can you give an example for the same in python

Ans. This means that the index value of -1 gives the last element, and -2 gives the second last element of an array. The negative indexing starts from where the array ends.
example: for list L = [0,2,35,3]; L[-1] will print 3 in Python.


Q4. What is the condition for using a t-test or a z-test?

Ans. z-test is used for it when sample size is large, generally n >30. Whereas t-test is used for hypothesis testing when sample size is small, usually n < 30 where n is used to quantify the sample size.


Q5. What is the main difference between overfitting and underfitting?

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.


Q6. What is the KNN imputation method?

Ans. The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.
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Role - Data Science Fresher

Company - Geakminds Technologies Private Limited

Exp - 0-1yr

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Today's Interview Quest N Ans

Company Name - TCS

Role - Data Scientist
________________

Q1. GMM vs K means?

Ans. Gaussian mixture models (GMMs) are often used for data clustering. You can use GMMs to perform either hard clustering or soft clustering on query data. To perform hard clustering, the GMM assigns query data points to the multivariate normal components that maximize the component posterior probability, given the data.

K-means clustering aims to partition data into k clusters in a way that data points in the same cluster are similar and data points in the different clusters are farther apart. Similarity of two points is determined by the distance between them.


Q2. Regression imputation?

Ans. Regression imputation fits a statistical model on a variable with missing values. Predictions of this regression model are used to substitute the missing values in this variable.


Q3. Z score for outliers treatment?

Ans. Z score test is one of the most commonly used methods to detect outliers. It measures the number of standard deviations away the observation is from the mean value. A z score of 1.5 indicated that the observation is 1.5 standard deviations above the mean and -1.5 means that the observation is 1.5 standard deviations below or less than the mean.


Q4. Handle data with lot of noise?

Ans. Noisy data can be handled by following the given procedures:

1) Binning:
• Binning methods smooth a sorted data value by consulting the values around it.
• The sorted values are distributed into a number of “buckets,” or bins.

2) Regression:
• Here data can be smoothed by fitting the data to a function.
• Linear regression involves finding the “best” line to fit two attributes, so that one attribute can be used to predict the other.
• Multiple linear regressionis an extension of linear regression, where more than two attributes are involved and the data are fit to a multidimensional surface.

3) Clustering:
• Outliers may be detected by clustering, where similar values are organized into groups, or “clusters.”



Q5. Min sample leaf and max depth in random forest?

Ans. Min sample leaf specifies the minimum number of samples that should be present in the leaf node after splitting a node. The max_depth of a tree in Random Forest is defined as the longest path between the root node and the leaf node. Using the max_depth parameter, I can limit up to what depth I want every tree in my random forest to grow.


Q6. Dict and list comprehension?

Ans. The only difference between list and dictionary comprehension is that the dictionary has the keys and values in it. So, the keys and values will come as the expression or value.

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