Filter interviews by
Top trending discussions
I applied via Campus Placement and was interviewed before Aug 2023. There were 2 interview rounds.
Had basic coding test
Use pandas library to read csv files in Python.
Import pandas library: import pandas as pd
Use pd.read_csv() function to read csv file
Specify file path as argument in read_csv() function
Assign the result to a variable to store the data
Example: df = pd.read_csv('file.csv')
I have used libraries like NumPy, Pandas, Matplotlib, and Scikit-learn in Python for data analysis and machine learning tasks.
NumPy: Used for numerical computing and array operations.
Pandas: Used for data manipulation and analysis.
Matplotlib: Used for data visualization.
Scikit-learn: Used for machine learning algorithms and model building.
I applied via Job Fair and was interviewed in Sep 2023. There was 1 interview round.
I delivered more than expected by implementing a new machine learning algorithm that significantly improved model accuracy.
Identified the need for a more advanced algorithm based on data analysis
Researched and implemented a cutting-edge machine learning algorithm
Tested the new algorithm on a sample dataset and compared results with existing models
Achieved a significant increase in model accuracy, exceeding initial expe
I applied via Campus Placement and was interviewed before May 2022. There were 4 interview rounds.
Test based on Python Programming nomenclatures and statistical concepts.
One topic to discuss based on latest in technology. For example Impact of AI on human jobs
Naive Bayes Algorithm is a simple probabilistic classifier based on Bayes' theorem with strong independence assumptions.
It is based on the assumption that the presence of a particular feature in a class is unrelated to the presence of any other feature.
It calculates the probability of each class given a set of input features and selects the class with the highest probability.
Commonly used in text classification, spam f...
Logistic Regression is a statistical method used to model the probability of a binary outcome.
Logistic Regression is used when the dependent variable is binary (e.g., 0 or 1, Yes or No).
It estimates the probability that a given input belongs to a certain category.
Assumptions of linear regression include linearity, independence of errors, homoscedasticity, and normality of errors.
Interview experience
Customer Engineer
310
salaries
| ₹2.5 L/yr - ₹6.4 L/yr |
Assistant Manager
161
salaries
| ₹5.2 L/yr - ₹17.2 L/yr |
Accounts Manager
139
salaries
| ₹6 L/yr - ₹18.2 L/yr |
Senior Engineer
129
salaries
| ₹4.5 L/yr - ₹19 L/yr |
Senior Customer Engineer
105
salaries
| ₹3.8 L/yr - ₹9 L/yr |
Otis Elevator
KONE
TK Elevator
Mitsubishi Electric