Date: 16-12-2022
Company name: Adecco
Role: Data Scientist
Topic: dendogram, time series, ribbon in excel, aggregate vs windows sql
1. What is a dendrogram in Hierarchical Clustering Algorithm?
A dendrogram is defined as a tree-like structure that is mainly used to store each step as a memory that the Hierarchical clustering algorithm performs. In the dendrogram plot, the Y-axis represents the Euclidean distances between the observations, and the X-axis represents all the observations present in the given dataset.
2. What are some common Data Preparation Operations you would use for Time Series Data?
Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.
3. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
4. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
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Company name: Adecco
Role: Data Scientist
Topic: dendogram, time series, ribbon in excel, aggregate vs windows sql
1. What is a dendrogram in Hierarchical Clustering Algorithm?
A dendrogram is defined as a tree-like structure that is mainly used to store each step as a memory that the Hierarchical clustering algorithm performs. In the dendrogram plot, the Y-axis represents the Euclidean distances between the observations, and the X-axis represents all the observations present in the given dataset.
2. What are some common Data Preparation Operations you would use for Time Series Data?
Parsing time series information from various sources and formats.
Generating sequences of fixed-frequency dates and time spans.
Manipulating and converting date times with time zone information.
Resampling or converting a time series to a particular frequency.
3. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
The main difference between window functions and aggregate functions is that aggregate functions group multiple rows into a single result row; all the individual rows in the group are collapsed and their individual data is not shown. On the other hand, window functions produce a result for each individual row. This result is usually shown as a new column value in every row within the window.
4. What is Ribbon in Excel and where does it appear?
The Ribbon is basically your key interface with Excel and it appears at the top of the Excel window. It allows users to access many of the most important commands directly. It consists of many tabs such as File, Home, View, Insert, etc. You can also customize the ribbon to suit your preferences. To customize the Ribbon, right-click on it and select the “Customize the Ribbon” option.
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Date: 17-12-2022
Company name: IKEA
Role: SQL Developer
Topic: Alias, pattern matching, drop&truncate, entity& relationship
1. Can you explain entities and relationships with examples?
An entity is a real-world object. A university database can have entities such as Student, Lecturer, Course, Project, etc. A relationship signifies an association among the entities. Example, that a relationship exists between the student and lecturer.
2. What is an Alias?
Aliases are the temporary names given to table or column for the purpose of a particular SQL query. It is used when name of column or table is used other than their original names, but the modified name is only temporary.
3. Which operator is used in query for pattern matching?
The LIKE operator provides standard pattern matching in SQL that is always used after a WHERE clause. It matches any pattern based on some conditions provided using the wildcard characters.
Some of the commonly used wildcard characters in MySQL are as follows:
'%' represents zero or more characters.
'_' represents exactly 1 character.
4. Explain the difference between drop and truncate.
In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.
IF YOU ARE NEW THEN DON'T FORGET TO JOIN OUR TELEGRAM CHANNEL FOR SUCH DATA SCIENCE & ANALYTICS DOMAIN UPDATES REGULARLY.
Company name: IKEA
Role: SQL Developer
Topic: Alias, pattern matching, drop&truncate, entity& relationship
1. Can you explain entities and relationships with examples?
An entity is a real-world object. A university database can have entities such as Student, Lecturer, Course, Project, etc. A relationship signifies an association among the entities. Example, that a relationship exists between the student and lecturer.
2. What is an Alias?
Aliases are the temporary names given to table or column for the purpose of a particular SQL query. It is used when name of column or table is used other than their original names, but the modified name is only temporary.
3. Which operator is used in query for pattern matching?
The LIKE operator provides standard pattern matching in SQL that is always used after a WHERE clause. It matches any pattern based on some conditions provided using the wildcard characters.
Some of the commonly used wildcard characters in MySQL are as follows:
'%' represents zero or more characters.
'_' represents exactly 1 character.
4. Explain the difference between drop and truncate.
In SQL, the DROP command is used to remove the whole database or table indexes, data, and more. Whereas the TRUNCATE command is used to remove all the rows from the table.
Date: 19-12-2022
Company name: TCS
Role: ML Engineer
Topic: KNN, Logistic Regression, RNN, LSTM
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
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Company name: TCS
Role: ML Engineer
Topic: KNN, Logistic Regression, RNN, LSTM
1. What is the Impact of Outliers on Logistic Regression?
The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.
2. What is the difference between vanilla RNNs and LSTMs?
The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.
3. What is Masked Language Model in NLP?
Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.
4. Why is the KNN Algorithm known as Lazy Learner?
When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.
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🚨JOB OPENING UPDATE🚨
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What will be the output of the following Python code snippet?
x = [i**+1 for i in range(3)]; print(x);
x = [i**+1 for i in range(3)]; print(x);
Anonymous Quiz
17%
[0, 1, 2]
29%
[1, 2, 5]
43%
error, **+ is not a valid operator
11%
error, ‘;’ is not allowed
❤10
Date: 21-12-2022
Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if
1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking
2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.
3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.
4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.
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Company name: Wipro
Role: Data Analyst
Topic: concurrency, checkpoint, groups, nested if
1. What is concurrency control in DBMS?
This is a process of managing simultaneous operations in a database so that database integrity is not compromised. The following are the two approaches involved in concurrency control:
Optimistic approach – Involves versioning
Pessimistic approach – Involves locking
2. What is a checkpoint in DBMS and when does it occur?
A checkpoint is a mechanism where all the previous logs are removed from the system and are permanently stored on the storage disk. So, basically, checkpoints are those points from where the transaction log record can be used to recover all the committed data up to the point of crash.
3. What are groups in Tableau?
A group is a combination of dimension members that make higher level categories. For example, if you are working with a view that shows average test scores by major, you may want to group certain majors together to create major categories.
4. How are nested IF statements used in Excel?
The function IF() can be nested when we have multiple conditions to meet. The FALSE value in the first IF function is replaced by another IF function to make a further test.
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
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Date: 22-12-2022
Company name: Infosys
Role: ML Engineer
Topic: regression,PCA, svm, multiclass vs multilabel
1. Compare Linear Regression and Decision Trees
Linear regression is used to predict continuous outputs where there is a linear relationship between the features of the dataset and the output variable.
Decision trees work by splitting the dataset, in a tree-like structure, into smaller and smaller subsets and make predictions based on which subset the new example falls into.
Linear regression is used for regression problems where it predicts something with infinite possible answers such as the price of a house.
Decision trees can be used to predict both regression and classification problems.
Linear regression is prone to underfitting the data. Switching to polynomial regression will sometimes help in countering underfitting.
Decision trees are prone to overfit the data. Pruning helps with the overfitting problem.
2. What are Support Vectors in SVMs?
Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane.
Using these support vectors, we maximize the margin of the classifier.
For computing predictions, only the support vectors are used.
3. How is the first principal component axis selected in PCA?
In Principal Component Analysis (PCA) we look to summarize a large set of correlated variables (basically a high dimensional data) into a smaller number of representative variables, called the principal components, that explains most of the variability in the original set.
The first principal component axis is selected in a way such that it explains most of the variation in the data and is closest to all n observations.
4. What is the difference between a Multiclass problem and a Multilabel problem?
Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.
Multilabel classification assigns to each sample a set of target labels. This can be thought of as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.
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Company name: Infosys
Role: ML Engineer
Topic: regression,PCA, svm, multiclass vs multilabel
1. Compare Linear Regression and Decision Trees
Linear regression is used to predict continuous outputs where there is a linear relationship between the features of the dataset and the output variable.
Decision trees work by splitting the dataset, in a tree-like structure, into smaller and smaller subsets and make predictions based on which subset the new example falls into.
Linear regression is used for regression problems where it predicts something with infinite possible answers such as the price of a house.
Decision trees can be used to predict both regression and classification problems.
Linear regression is prone to underfitting the data. Switching to polynomial regression will sometimes help in countering underfitting.
Decision trees are prone to overfit the data. Pruning helps with the overfitting problem.
2. What are Support Vectors in SVMs?
Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane.
Using these support vectors, we maximize the margin of the classifier.
For computing predictions, only the support vectors are used.
3. How is the first principal component axis selected in PCA?
In Principal Component Analysis (PCA) we look to summarize a large set of correlated variables (basically a high dimensional data) into a smaller number of representative variables, called the principal components, that explains most of the variability in the original set.
The first principal component axis is selected in a way such that it explains most of the variation in the data and is closest to all n observations.
4. What is the difference between a Multiclass problem and a Multilabel problem?
Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.
Multilabel classification assigns to each sample a set of target labels. This can be thought of as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.
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Date: 23-12-2022
Company name: Elica
Role: Data Scientist
Topic: kmeans, SQL vs nosql, LSTM, powerbi
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. If your Time-Series Dataset is very long, what architecture would you use?
If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.
3. How would you define Power BI as an effective solution ?
Power BI is a strong business analytical tool that creates useful insights and reports by collating data from unrelated sources. This data can be extracted from any source like Microsoft Excel or hybrid data warehouses. Power BI drives an extreme level of utility and purpose using interactive graphical interface and visualizations.
4. Explain NoSQL vs SQL
In summary, the following are the five major distinctions between SQL and NoSQL:
Relational databases are SQL, while non-relational databases are NoSQL.
SQL databases have a specified schema and employ structured query language. For unstructured data, NoSQL databases use dynamic schemas.
SQL databases scale vertically, but NoSQL databases scale horizontally.
NoSQL databases are document, key-value, graph, or wide-column stores, whereas SQL databases are table-based
Company name: Elica
Role: Data Scientist
Topic: kmeans, SQL vs nosql, LSTM, powerbi
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. If your Time-Series Dataset is very long, what architecture would you use?
If the dataset for time-series is very long, LSTMs are ideal for it because it can not only process single data points, but also entire sequences of data. A time-series being a sequence of data makes LSTM ideal for it.For an even stronger representational capacity, making the LSTM's multi-layered is better.Another method for long time-series dataset is to use CNNs to extract information.
3. How would you define Power BI as an effective solution ?
Power BI is a strong business analytical tool that creates useful insights and reports by collating data from unrelated sources. This data can be extracted from any source like Microsoft Excel or hybrid data warehouses. Power BI drives an extreme level of utility and purpose using interactive graphical interface and visualizations.
4. Explain NoSQL vs SQL
In summary, the following are the five major distinctions between SQL and NoSQL:
Relational databases are SQL, while non-relational databases are NoSQL.
SQL databases have a specified schema and employ structured query language. For unstructured data, NoSQL databases use dynamic schemas.
SQL databases scale vertically, but NoSQL databases scale horizontally.
NoSQL databases are document, key-value, graph, or wide-column stores, whereas SQL databases are table-based
Date: 24-12-2022
Company name: Datanyze
Role: SQL Developer
Topic: col rename, like , constraint
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
Company name: Datanyze
Role: SQL Developer
Topic: col rename, like , constraint
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
❤1
Date: 26-12-2022
Company name: Flipkart
Role: ML Engineer
Topic: svm, knn, gini entropy, hierarchical clustering
1. Compare SVM and Logistic Regression in handling outliers
For Logistic Regression, outliers can have an unusually large effect on the estimate of logistic regression coefficients. It will find a linear boundary if it exists to accommodate the outliers. To solve the problem of outliers, sometimes a sigmoid function is used in logistic regression.
For SVM, outliers can make the decision boundary deviate severely from the optimal hyperplane. One way for SVM to get around the problem is to introduce slack variables. There is a penalty involved with using slack variables, and how SVM handles outliers depends on how this penalty is imposed.
2. Can you explain how to implement a simple kNN algorithm in code?
The kNN algorithm can be used for a variety of tasks (classification & regression). To implement it, you will need to first calculate the distance between the new data point and all of the training data points. Once you have the distances, you will then need to find the k nearest neighbors and take the majority vote of those neighbors to determine the class of the new data point.
3. What type of node is considered Pure in the decision tree?
If the Gini Index of the data is 0 then it means that all the elements belong to a specific class. When this happens it is said to be pure.
When all of the data belongs to a single class (pure) then the leaf node is reached in the tree.
The leaf node represents the class label in the tree (which means that it gives the final output).
4. What is Space and Time Complexity of the Hierarchical Clustering Algorithm?
Space complexity: Hierarchical Clustering Technique requires very high space when the number of observations in our dataset is more since we need to store the similarity matrix in the RAM. So, the space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of observations.
Time complexity: Since we have to perform n iterations and in each iteration, we need to update the proximity matrix and also restore that matrix, therefore the time complexity is also very high. So, the time complexity is the order of the cube of n. Time complexity = O(n³) where n is the number of observations.
Company name: Flipkart
Role: ML Engineer
Topic: svm, knn, gini entropy, hierarchical clustering
1. Compare SVM and Logistic Regression in handling outliers
For Logistic Regression, outliers can have an unusually large effect on the estimate of logistic regression coefficients. It will find a linear boundary if it exists to accommodate the outliers. To solve the problem of outliers, sometimes a sigmoid function is used in logistic regression.
For SVM, outliers can make the decision boundary deviate severely from the optimal hyperplane. One way for SVM to get around the problem is to introduce slack variables. There is a penalty involved with using slack variables, and how SVM handles outliers depends on how this penalty is imposed.
2. Can you explain how to implement a simple kNN algorithm in code?
The kNN algorithm can be used for a variety of tasks (classification & regression). To implement it, you will need to first calculate the distance between the new data point and all of the training data points. Once you have the distances, you will then need to find the k nearest neighbors and take the majority vote of those neighbors to determine the class of the new data point.
3. What type of node is considered Pure in the decision tree?
If the Gini Index of the data is 0 then it means that all the elements belong to a specific class. When this happens it is said to be pure.
When all of the data belongs to a single class (pure) then the leaf node is reached in the tree.
The leaf node represents the class label in the tree (which means that it gives the final output).
4. What is Space and Time Complexity of the Hierarchical Clustering Algorithm?
Space complexity: Hierarchical Clustering Technique requires very high space when the number of observations in our dataset is more since we need to store the similarity matrix in the RAM. So, the space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of observations.
Time complexity: Since we have to perform n iterations and in each iteration, we need to update the proximity matrix and also restore that matrix, therefore the time complexity is also very high. So, the time complexity is the order of the cube of n. Time complexity = O(n³) where n is the number of observations.
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Date: 27-12-2022
Company name: Flipkart
Role: ML Engineer
Topic: svm, knn, gini entropy, hierarchical clustering
1. Compare SVM and Logistic Regression in handling outliers
For Logistic Regression, outliers can have an unusually large effect on the estimate of logistic regression coefficients. It will find a linear boundary if it exists to accommodate the outliers. To solve the problem of outliers, sometimes a sigmoid function is used in logistic regression.
For SVM, outliers can make the decision boundary deviate severely from the optimal hyperplane. One way for SVM to get around the problem is to introduce slack variables. There is a penalty involved with using slack variables, and how SVM handles outliers depends on how this penalty is imposed.
2. Can you explain how to implement a simple kNN algorithm in code?
The kNN algorithm can be used for a variety of tasks (classification & regression). To implement it, you will need to first calculate the distance between the new data point and all of the training data points. Once you have the distances, you will then need to find the k nearest neighbors and take the majority vote of those neighbors to determine the class of the new data point.
3. What type of node is considered Pure in the decision tree?
If the Gini Index of the data is 0 then it means that all the elements belong to a specific class. When this happens it is said to be pure.
When all of the data belongs to a single class (pure) then the leaf node is reached in the tree.
The leaf node represents the class label in the tree (which means that it gives the final output).
4. What is Space and Time Complexity of the Hierarchical Clustering Algorithm?
Space complexity: Hierarchical Clustering Technique requires very high space when the number of observations in our dataset is more since we need to store the similarity matrix in the RAM. So, the space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of observations.
Time complexity: Since we have to perform n iterations and in each iteration, we need to update the proximity matrix and also restore that matrix, therefore the time complexity is also very high. So, the time complexity is the order of the cube of n. Time complexity = O(n³) where n is the number of observations.
IF YOU ARE NEW ON THIS TELEGRAM CHANNEL THEN DON'T FORGET TO JOIN FOR SUCH DATA SCIENCE & ANALYTICS DOMAIN UPDATES REGULARLY.
Company name: Flipkart
Role: ML Engineer
Topic: svm, knn, gini entropy, hierarchical clustering
1. Compare SVM and Logistic Regression in handling outliers
For Logistic Regression, outliers can have an unusually large effect on the estimate of logistic regression coefficients. It will find a linear boundary if it exists to accommodate the outliers. To solve the problem of outliers, sometimes a sigmoid function is used in logistic regression.
For SVM, outliers can make the decision boundary deviate severely from the optimal hyperplane. One way for SVM to get around the problem is to introduce slack variables. There is a penalty involved with using slack variables, and how SVM handles outliers depends on how this penalty is imposed.
2. Can you explain how to implement a simple kNN algorithm in code?
The kNN algorithm can be used for a variety of tasks (classification & regression). To implement it, you will need to first calculate the distance between the new data point and all of the training data points. Once you have the distances, you will then need to find the k nearest neighbors and take the majority vote of those neighbors to determine the class of the new data point.
3. What type of node is considered Pure in the decision tree?
If the Gini Index of the data is 0 then it means that all the elements belong to a specific class. When this happens it is said to be pure.
When all of the data belongs to a single class (pure) then the leaf node is reached in the tree.
The leaf node represents the class label in the tree (which means that it gives the final output).
4. What is Space and Time Complexity of the Hierarchical Clustering Algorithm?
Space complexity: Hierarchical Clustering Technique requires very high space when the number of observations in our dataset is more since we need to store the similarity matrix in the RAM. So, the space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of observations.
Time complexity: Since we have to perform n iterations and in each iteration, we need to update the proximity matrix and also restore that matrix, therefore the time complexity is also very high. So, the time complexity is the order of the cube of n. Time complexity = O(n³) where n is the number of observations.
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Date: 28-12-2022
Company name: Flipkart
Role: ML Engineer
Topic: svm, knn, gini entropy, hierarchical clustering
1. Compare SVM and Logistic Regression in handling outliers
For Logistic Regression, outliers can have an unusually large effect on the estimate of logistic regression coefficients. It will find a linear boundary if it exists to accommodate the outliers. To solve the problem of outliers, sometimes a sigmoid function is used in logistic regression.
For SVM, outliers can make the decision boundary deviate severely from the optimal hyperplane. One way for SVM to get around the problem is to introduce slack variables. There is a penalty involved with using slack variables, and how SVM handles outliers depends on how this penalty is imposed.
2. Can you explain how to implement a simple kNN algorithm in code?
The kNN algorithm can be used for a variety of tasks (classification & regression). To implement it, you will need to first calculate the distance between the new data point and all of the training data points. Once you have the distances, you will then need to find the k nearest neighbors and take the majority vote of those neighbors to determine the class of the new data point.
3. What type of node is considered Pure in the decision tree?
If the Gini Index of the data is 0 then it means that all the elements belong to a specific class. When this happens it is said to be pure.
When all of the data belongs to a single class (pure) then the leaf node is reached in the tree.
The leaf node represents the class label in the tree (which means that it gives the final output).
4. What is Space and Time Complexity of the Hierarchical Clustering Algorithm?
Space complexity: Hierarchical Clustering Technique requires very high space when the number of observations in our dataset is more since we need to store the similarity matrix in the RAM. So, the space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of observations.
Time complexity: Since we have to perform n iterations and in each iteration, we need to update the proximity matrix and also restore that matrix, therefore the time complexity is also very high. So, the time complexity is the order of the cube of n. Time complexity = O(n³) where n is the number of observations.
IF YOU ARE NEW ON THIS TELEGRAM CHANNEL THEN DON'T FORGET TO JOIN FOR SUCH DATA SCIENCE & ANALYTICS DOMAIN UPDATES REGULARLY.
Company name: Flipkart
Role: ML Engineer
Topic: svm, knn, gini entropy, hierarchical clustering
1. Compare SVM and Logistic Regression in handling outliers
For Logistic Regression, outliers can have an unusually large effect on the estimate of logistic regression coefficients. It will find a linear boundary if it exists to accommodate the outliers. To solve the problem of outliers, sometimes a sigmoid function is used in logistic regression.
For SVM, outliers can make the decision boundary deviate severely from the optimal hyperplane. One way for SVM to get around the problem is to introduce slack variables. There is a penalty involved with using slack variables, and how SVM handles outliers depends on how this penalty is imposed.
2. Can you explain how to implement a simple kNN algorithm in code?
The kNN algorithm can be used for a variety of tasks (classification & regression). To implement it, you will need to first calculate the distance between the new data point and all of the training data points. Once you have the distances, you will then need to find the k nearest neighbors and take the majority vote of those neighbors to determine the class of the new data point.
3. What type of node is considered Pure in the decision tree?
If the Gini Index of the data is 0 then it means that all the elements belong to a specific class. When this happens it is said to be pure.
When all of the data belongs to a single class (pure) then the leaf node is reached in the tree.
The leaf node represents the class label in the tree (which means that it gives the final output).
4. What is Space and Time Complexity of the Hierarchical Clustering Algorithm?
Space complexity: Hierarchical Clustering Technique requires very high space when the number of observations in our dataset is more since we need to store the similarity matrix in the RAM. So, the space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of observations.
Time complexity: Since we have to perform n iterations and in each iteration, we need to update the proximity matrix and also restore that matrix, therefore the time complexity is also very high. So, the time complexity is the order of the cube of n. Time complexity = O(n³) where n is the number of observations.
Date: 29-12-2022
Company name: Blinkit
Role: Data Scientist
Topic: kmean, filters in tableau, decorators in python, subquery in sql
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. What are decorators in Python?
Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
3. What is a Subquery in SQL? What are its types?
A subquery is a query within another query, also known as a nested query or inner query. It is used to restrict or enhance the data to be queried by the main query, thus restricting or enhancing the output of the main query respectively. There are two types of subquery - Correlated and Non-Correlated.
A correlated subquery cannot be considered as an independent query, but it can refer to the column in a table listed in the FROM of the main query.
A non-correlated subquery can be considered as an independent query and the output of the subquery is substituted in the main query.
4. What are the various types of filters in Tableau?
Tableau has 6 different types of filters:
Extract Filter: This filter retrieves a subset of data from the data source.
Dimension Filter: This filter is for non-aggregated data (discrete).
Data Source Filter: This filter refrains users from viewing sensitive information and thus reduces data feeds.
Context Filter: This filter creates datasets by applying presets in Tableau.
Measure Filter: This filter applies various operations like sum, median, avg, etc.
Table Calculation Filter: This filter is applied after the view has been created
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Company name: Blinkit
Role: Data Scientist
Topic: kmean, filters in tableau, decorators in python, subquery in sql
1. Explain some cases where k-Means clustering fails to give good results
k-means has trouble clustering data where clusters are of various sizes and densities.
Outliers will cause the centroids to be dragged, or the outliers might get their own cluster instead of being ignored. Outliers should be clipped or removed before clustering.
If the number of dimensions increase, a distance-based similarity measure converges to a constant value between any given examples. Dimensions should be reduced before clustering them.
2. What are decorators in Python?
Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.
3. What is a Subquery in SQL? What are its types?
A subquery is a query within another query, also known as a nested query or inner query. It is used to restrict or enhance the data to be queried by the main query, thus restricting or enhancing the output of the main query respectively. There are two types of subquery - Correlated and Non-Correlated.
A correlated subquery cannot be considered as an independent query, but it can refer to the column in a table listed in the FROM of the main query.
A non-correlated subquery can be considered as an independent query and the output of the subquery is substituted in the main query.
4. What are the various types of filters in Tableau?
Tableau has 6 different types of filters:
Extract Filter: This filter retrieves a subset of data from the data source.
Dimension Filter: This filter is for non-aggregated data (discrete).
Data Source Filter: This filter refrains users from viewing sensitive information and thus reduces data feeds.
Context Filter: This filter creates datasets by applying presets in Tableau.
Measure Filter: This filter applies various operations like sum, median, avg, etc.
Table Calculation Filter: This filter is applied after the view has been created
5. Do you wanna make your career in Data Science & Analytics but don't know how to start ?
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Check India's Most Affordable Industry Oriented Course, chosen & trusted by 12000+ happy learners. Link Below👇🏻
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