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Angel One Data Scientist 2 Interview Questions, Process, and Tips
Angel One Data Scientist 2 Interview Experiences
1 interview found
Anonymous
I applied via Instahyre and was interviewed in Feb 2024. There were 2 interview rounds.
(2 Questions)
- Q1. 1. How to handle model overfitting and model underfitting situations ? 2. In which situations should we standardize the data and where it is not required ? 3. How does Decision Trees work ? 4. How to selec...
- Ans.
Answering common questions related to data science concepts and techniques.
To handle model overfitting, one can use techniques like cross-validation, regularization, and early stopping. For model underfitting, consider using more complex models or adding more features.
Standardizing data is important for algorithms like K-Nearest Neighbors and Support Vector Machines. It is not required for tree-based models like Decisi...
- Q2. 1. If you already have a lot of features and you also have categorical column what strategy will you use to encode the categorical column so that the overall feature count should not increase or should not...
- Ans.
Use target encoding or frequency encoding to encode categorical columns without increasing feature count.
Use target encoding: Encode categorical column with the mean of the target variable for each category.
Use frequency encoding: Encode categorical column with the frequency of each category in the dataset.
Both methods preserve the information of the categorical column without increasing feature count.
(2 Questions)
- Q1. 1. What are the assumptions of Linear Regression ? 2. What is the formula for Euclidean distance in K-Means ? 3. How does SVM work ? 4. How does SVM work on non linearly separable data ?
- Ans.
Answers to questions related to Linear Regression, K-Means, and SVM in data science.
Assumptions of Linear Regression include linearity, independence, homoscedasticity, and normality of errors.
Euclidean distance formula in K-Means is the square root of the sum of squared differences between two points.
SVM works by finding the hyperplane that best separates the classes in the feature space.
SVM on non-linearly separable d...
- Q2. What is the difference between Decision Trees and Random Forest. Why do we grow a lot of Decision Trees in a Random Forest ?
- Ans.
Decision Trees are single trees while Random Forest is a collection of trees. Random Forest grows multiple trees to improve accuracy and reduce overfitting.
Decision Trees are individual trees that make decisions based on features of the data.
Random Forest is an ensemble method that combines multiple Decision Trees to improve accuracy and reduce overfitting.
Random Forest grows a lot of Decision Trees to increase diversi...
Interview Preparation Tips
Skills evaluated in this interview
Top trending discussions
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Selected
I applied via Referral and was interviewed before Feb 2023. There were 2 interview rounds.
Credit risk default prediction
Assignment discussion and ML depth
Interview Preparation Tips
- Machine Learning
- Python
- Credit Risk
I applied via Company Website and was interviewed in Mar 2024. There were 2 interview rounds.
Pyspark, Python and Machine learning questions
(2 Questions)
- Q1. Moderate level questions
- Q2. Moderate level questions from past work experience
Interview Preparation Tips
I even tried reaching them but there was no reply
I applied via LinkedIn and was interviewed in Dec 2023. There was 1 interview round.
(2 Questions)
- Q1. Discussion on linear regression and regularisation techniques
- Q2. Discussion on tree based models and measures to score the models
Interview Preparation Tips
- Regression
- Machine Learning
(2 Questions)
- Q1. DBSCAN Clustering
- Q2. How does current work helps solving use cases for PayU
- Ans.
My current work involves analyzing transaction data to identify patterns and trends, which can help PayU optimize their payment processing services.
Analyzing transaction data to identify fraudulent activities and improve security measures for PayU
Developing predictive models to forecast transaction volumes and optimize payment processing times
Utilizing machine learning algorithms to personalize user experiences and inc...
(2 Questions)
- Q1. Details about past projects
- Q2. Details about latest project
Interview Preparation Tips
Selected
I applied via campus placement at Kalinga Institute of Industrial Technology, Khurda and was interviewed before Apr 2022. There were 2 interview rounds.
(2 Questions)
- Q1. Basic ML questions you can get those on any website by searching ML/Ds question
- Q2. Some deep question on python like unpacking in python etc
Interview Preparation Tips
Aditya Patankar
(2 Questions)
- Q1. What is deep learning?
- Ans.
Deep learning is a subset of machine learning that uses neural networks to learn from data.
Deep learning involves training neural networks with large amounts of data to make predictions or decisions.
It is used in image recognition, natural language processing, and speech recognition.
Deep learning models can automatically learn to extract features from raw data.
It requires a lot of computational power and data to train ...
- Q2. Advanced Branch of ai
- Ans.
Reinforcement Learning is an advanced branch of AI that involves training agents to make decisions based on rewards and punishments.
Reinforcement Learning involves an agent interacting with an environment and learning from the rewards and punishments it receives.
It has been used in various applications such as game playing, robotics, and recommendation systems.
Some popular algorithms in Reinforcement Learning include Q...
(2 Questions)
- Q1. Expected salary
- Q2. Shared the salary as needed
Skills evaluated in this interview
Akshay Agrawal
I applied via LinkedIn and was interviewed in Jun 2022. There were 2 interview rounds.
(3 Questions)
- Q1. What is rnn and lstm
- Ans.
RNN stands for Recurrent Neural Network and LSTM stands for Long Short-Term Memory. They are types of neural networks used for sequential data processing.
RNN is a type of neural network that can process sequential data by maintaining a memory of past inputs.
LSTM is a type of RNN that can handle the vanishing gradient problem and can remember long-term dependencies.
LSTM has gates that control the flow of information int...
- Q2. What is precision and recall
- Ans.
Precision and recall are two important metrics used to evaluate the performance of a classification model.
Precision measures the proportion of true positives among all the predicted positives.
Recall measures the proportion of true positives among all the actual positives.
Precision and recall are inversely related and a trade-off exists between them.
A high precision means that the model is good at predicting positive ca...
- Q3. What is regularisation
- Ans.
Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.
Regularisation helps to control the complexity of a model and reduce the impact of irrelevant features.
It adds a penalty term to the loss function, which encourages the model to have smaller weights.
There are different types of regularisation techniques such as L1 (Lasso) and L2 (Ridge) regularisa...
Interview Preparation Tips
- Deep Learning
- Machine Learning
- SQL
- Python
Skills evaluated in this interview
InfoOnly
I applied via Referral and was interviewed in May 2024. There was 1 interview round.
(2 Questions)
- Q1. Why are you interested in this role
- Q2. Provide an example how you solved a problem
- Ans.
I solved a problem by using machine learning algorithms to predict customer churn for a telecom company.
Identified relevant data sources such as customer demographics, usage patterns, and customer service interactions.
Preprocessed and cleaned the data to handle missing values and outliers.
Built and trained a machine learning model using algorithms like logistic regression and random forest.
Evaluated the model's perform...
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Angel One Data Scientist 2 Interview Process
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