10+ Interview Questions and Answers

Code to identify prime numbers between two given numbers.

• Create a function that takes two numbers as input.

• Loop through the range of numbers between the two inputs.

• Check if each number is divisible by any number other than 1 and itself.

• If not, add it to a list of prime numbers.

• Return the list of prime numbers.

Outliers can be handled by removing, transforming or imputing them. Imbalanced datasets can be handled by resampling techniques. Feature engineering involves creating new features from existing ones.

• Outliers can be removed using statistical methods like z-score or IQR.

• Outliers can be transformed using techniques like log transformation or box-cox transformation.

• Outliers can be imputed using techniques like mean imputation or regression imputation.

• Imbalanced datasets can be ha...read more

I am proficient in several technologies including Python, SQL, and Tableau.

• Python

• SQL

• Tableau

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.

• It assumes a linear relationship between the variables

• It is used to predict the value of the dependent variable based on the independent variable(s)

• It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)

• It is commonly used in fields such as finance, economics, and social ...read more

Machine learning algorithms are used to train models on data to make predictions or decisions.

• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve an agent learning through trial and error.

• Examples of machine learning applications include image recognition, natural language processing, and fraud detection.

Overfitting occurs when a model learns the training data too well, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

• Overfitting: Model is too complex, fits noise in the training data, performs poorly on new data

• Underfitting: Model is too simple, fails to capture underlying patterns in the data, performs poorly on both training and new data

• Examples: Overfitting - Decision tree with too many b...read more

Model evaluation techniques are used to assess the performance of a machine learning model.

• Common techniques include cross-validation, holdout validation, and bootstrap validation.

• Metrics such as accuracy, precision, recall, and F1 score can be used to evaluate model performance.

• Visualizations such as confusion matrices and ROC curves can also aid in model evaluation.

• It is important to use multiple evaluation techniques and compare results to ensure robustness of the model.

ARM is preferred for low-power devices and embedded systems.

• ARM processors are commonly used in smartphones, tablets, and IoT devices for their energy efficiency.

• ARM architecture is suitable for applications that require low power consumption and high performance.

• ARM-based chips are often chosen for embedded systems due to their compact size and low heat generation.

ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Random Forest

• Naive Bayes

• Decision Trees

• Linear Regression

• Logistic Regression

• Neural Networks

• Gradient Boosting

• Clustering Algorithms (K-Means, Hierarchical)

• Association Rule Learning (Apriori)

• Dimensionality Reduction Algorithms (PCA, LDA)

• Reinforcement Learning

• Deep Learning (CNN, RNN, LSTM)

The project involved exploratory data analysis (EDA) to gain insights and identify patterns in the data.

• Performed data cleaning and preprocessing

• Visualized data using various charts and graphs

• Identified correlations and relationships between variables

• Used statistical methods to analyze data

• Generated hypotheses for further analysis

RF algorithm is chosen for its ability to handle large datasets, high accuracy, and resistance to overfitting.

• RF algorithm is an ensemble learning method that builds multiple decision trees and merges them together to improve accuracy.

• It can handle large datasets with high dimensionality and is less prone to overfitting compared to other algorithms.

• RF algorithm is versatile and can be used for both classification and regression tasks.

• It provides feature importance which helps...read more

Find Nth-largest element in an array

• Sort the array in descending order

• Return the element at index N-1

LLM models, or Language Model Models, are a type of machine learning model that focuses on predicting the next word in a sequence of words.

• LLM models are commonly used in natural language processing tasks such as text generation, machine translation, and speech recognition.

• They are trained on large amounts of text data to learn the relationships between words and predict the most likely next word in a given context.

• Examples of LLM models include GPT-3 (Generative Pre-trained ...read more