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I applied via LinkedIn and was interviewed before Oct 2023. There were 2 interview rounds.
Graph based question, acyclic graph
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180 mins of online test with camera ON. Major topics include Excel, Aptitude, Python, Statistics and Case Study
Apriori method is a popular algorithm for frequent itemset mining in data mining.
Used for finding frequent itemsets in transactional databases
Based on the concept of association rule mining
Involves generating candidate itemsets and pruning based on support threshold
Example: If {milk, bread} is a frequent itemset, then {milk} and {bread} are also frequent
Train-test split is a method used to divide a dataset into training and testing sets for model evaluation in Scikit learn.
Split the dataset into two subsets: training set and testing set
Training set is used to train the model, while testing set is used to evaluate the model's performance
Common split ratios are 70-30 or 80-20 for training and testing sets
Example: X_train, X_test, y_train, y_test = train_test_split(X, y,
I applied via Approached by Company and was interviewed before Sep 2021. There were 3 interview rounds.
Explain dynamic programming with memoization
I applied via Approached by Company and was interviewed before Apr 2023. There was 1 interview round.
Geometric algorithm patterns involve solving problems related to geometric shapes and structures.
Identifying and solving problems related to points, lines, angles, and shapes
Utilizing geometric formulas and theorems to find solutions
Examples include calculating area, perimeter, angles, and distances in geometric figures
I would train a decision tree model as it can handle categorical data well with minimal data.
Decision tree models are suitable for categorical prediction with minimal data
They can handle both numerical and categorical data
Decision trees are easy to interpret and visualize
Examples: predicting customer churn, classifying spam emails
I was interviewed before Mar 2023.
K-means is a clustering algorithm while KNN is a classification algorithm.
K-means is unsupervised learning, KNN is supervised learning
K-means partitions data into K clusters based on distance, KNN classifies data points based on similarity to K neighbors
K-means requires specifying the number of clusters (K), KNN requires specifying the number of neighbors (K)
Example: K-means can be used to group customers based on purc...
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Thomson Reuters
Elsevier
Wolters Kluwer
Springer Nature in India