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It was quite easy it's the 1:30 hr time based test with no negative marking and the total of 60 questions
I applied via campus placement at Mumbai University and was interviewed in Jan 2023. There were 2 interview rounds.
It was held at my college campus.
First round was aptitude test, can easily score
Second was Hr round (final)
Not a single question was asked on technical
Nothing much basic mathematical questions
Fascinated by the insights and patterns that can be extracted from data to drive decision-making and innovation.
Passionate about uncovering hidden trends and correlations in data
Excited about using data to solve complex problems and make informed decisions
Believe in the power of data-driven decision-making to drive business success
I applied via Instagram
Aptitude test regarding the maths and others
TuringMinds interview questions for designations
I applied via campus placement at SBCE Sree Buddha College of Engineering, Alappuzha and was interviewed in Feb 2023. There were 2 interview rounds.
Basic questions involving age, patterns, profit/loss, trains, boats/streams, positions
I applied via Company Website and was interviewed in Aug 2022. There were 3 interview rounds.
Reasoning,time and distance, apptitude,paragraphs
Top trending discussions
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model c...
Parameters of a Decision Tree include max depth, min samples split, criterion, and splitter.
Max depth: maximum depth of the tree
Min samples split: minimum number of samples required to split an internal node
Criterion: function to measure the quality of a split (e.g. 'gini' or 'entropy')
Splitter: strategy used to choose the split at each node (e.g. 'best' or 'random')
Developed a predictive model to forecast customer churn in a telecom company
Collected and cleaned customer data including usage patterns and demographics
Used machine learning algorithms such as logistic regression and random forest to build the model
Evaluated model performance using metrics like accuracy, precision, and recall
Provided actionable insights to the company to reduce customer churn rate
I applied via Referral and was interviewed in Nov 2024. There was 1 interview round.
I was interviewed in Oct 2024.
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
Transfer learning uses knowledge gained from one task to improve learning on a different task.
Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.
Transfer learning is faster and requires less data compared to training a...
I applied via Approached by Company and was interviewed in Aug 2024. There were 2 interview rounds.
*****, arjumpudi satyanarayana
Python is a high-level programming language known for its simplicity and readability.
Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.
It emphasizes code readability and uses indentation for block delimiters.
Python has a large standard library and a vibrant community of developers.
Example: print('Hello, World!')
Example: import pandas as pd
Code problems refer to issues or errors in the code that need to be identified and fixed.
Code problems can include syntax errors, logical errors, or performance issues.
Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.
Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.
Python code is a programming language used for data analysis, machine learning, and scientific computing.
Python code is written in a text editor or an integrated development environment (IDE)
Python code is executed using a Python interpreter
Python code can be used for data manipulation, visualization, and modeling
The project is a machine learning model to predict customer churn for a telecommunications company.
Developing predictive models using machine learning algorithms
Analyzing customer data to identify patterns and trends
Evaluating model performance and making recommendations for reducing customer churn
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based on 8 interviews
3 Interview rounds
based on 42 reviews
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